Methods and apparatus for displaying diagnostic data

ABSTRACT

The invention provides methods for displaying diagnostic results obtained from a tissue sample. In general, the invention assigns tissue-class probability values to discrete regions of a patient sample, and creates an overlay for displaying the results. The overlay facilitates display of the tissue class probabilities in a way that reflects the diagnostic relevance of the data. For example, methods of the invention comprise applying filtering and color-blending techniques in order to facilitate display of diagnostic results.

PRIOR APPLICATIONS

This application is related to the following commonly-ownedapplications: Ser. No. 10/418,922, entitled, “Methods and Apparatus forCharacterization of Tissue Samples”; Ser. No. 10/418,974, entitled,“Methods and Apparatus for Visually Enhancing Images”; Ser. No.10/418,668, entitled, “Methods and Apparatus for Characterization ofTissue Samples”; Ser. No. 10/418,975, entitled, “Methods and Apparatusfor Processing Image Data for Use in Tissue Characterization”; Ser. No.10/418,415, entitled, “Methods and Apparatus for Processing SpectralData for Use in Tissue Characterization”; Ser. No. 10/419,181, entitled,“Methods and Apparatus for Evaluating Image Focus”; and Ser. No.10/418,973, entitled, “Methods and Apparatus for Calibrating SpectralData,” all of which are filed on even date herewith.

FIELD OF THE INVENTION

This invention relates generally to data display methods. Moreparticularly, in certain embodiments, the invention relates to thedisplay of diagnostic data from a tissue classification algorithm.

BACKGROUND OF THE INVENTION

It is common in the field of medicine to perform visual examination todiagnose disease. For example, visual examination of the cervix candiscern areas where there is a suspicion of pathology. However, directvisual observation alone may be inadequate for proper identification ofan abnormal tissue sample, particularly in the early stages of disease.

In some procedures, such as colposcopic examinations, a chemical agent,such as acetic acid, is applied to enhance the differences in appearancebetween normal and pathological tissue. Such acetowhitening techniquesmay aid a colposcopist in the determination of areas in which there is asuspicion of pathology.

Colposcopic techniques are not perfect. They generally require analysisby a highly-trained physician. Colposcopic images may contain complexand confusing patterns and may be affected by glare, shadow, or thepresence of blood or other obstruction, rendering an indeterminatediagnosis.

Spectral analysis has increasingly been used to diagnose disease intissue. Spectral analysis is based on the principle that the intensityof light that is transmitted from an illuminated tissue sample mayindicate the state of health of the tissue. As in colposcopicexamination, spectral analysis of tissue may be conducted using acontrast agent such as acetic acid. In spectral analysis, the contrastagent is used to enhance differences in the light that is transmittedfrom normal and pathological tissues.

Spectral analysis offers the prospect of at least partially-automateddiagnosis of tissue using a classification algorithm. However,examinations using spectral analysis may be adversely affected by glare,shadow, or the presence of blood or other obstruction, rendering anindeterminate diagnosis. Some artifacts may not be detectable byanalysis of the spectral data alone; hence, erroneous spectral data maybe inseparable from valid spectral data. Also, the surface of a tissuesample under spectral examination is generally not homogeneous. Areas ofdisease may be interspersed among neighboring healthy tissue, renderingoverly-diffuse spectral data erroneous.

Furthermore, current methods of displaying data based on tissueclassification algorithms do not facilitate quick, accurate, or clearcommunication of diagnostic results. Current techniques generallyrequire interpretation by a skilled medical professional for meaningfuland accurate conveyance of diagnostic information, due, in part, to theunfiltered nature of the diagnostic data.

Thus, there exists a need to improve the ease, accuracy, and claritywith which diagnostic data are displayed.

SUMMARY OF THE INVENTION

The invention provides methods for displaying diagnostic resultsobtained from a tissue sample. In general, the invention assignstissue-class probability values to discrete regions of a patient sample,and creates an overlay for displaying the results. One feature of theoverlay is that it facilitates display of the tissue class probabilitiesin a way that reflects the diagnostic relevance of the data. Forexample, methods of the invention comprise applying filtering andcolor-blending techniques in order to facilitate display of diagnosticresults. Those techniques enhance certain portions of the overlay inorder to highlight diagnostically-relevant regions of the sample.

Further increases in diagnostic relevance are obtained when the overlayis viewed as a composite that includes a reference image of the sample.For example, preferred methods of the invention represent a range oftissue-class probabilities as a spectral blend between two colors thatcontrast with an average tissue color. In one embodiment, a portion ofthe spectrum representing low probability of disease is blended with anaverage tissue color so that tissue regions associated with a lowprobability of disease are featured less prominently in the composite.

Preferred embodiments of the invention comprise application ofdiagnostic data that properly account for indeterminate regions of atissue sample. A region may be diagnosed as indeterminate if it isaffected by an obstruction or if it lies outside a zone of diagnosticinterest. A region of a tissue sample may be obstructed, for example, bymucus, fluid, foam, a medical instrument, glare, shadow, and/or blood.Regions that lie outside a zone of interest include, for example, atissue wall (e.g., a vaginal wall), an os, an edge surface of a tissue(e.g., a cervical edge), and tissue in the vicinity of a smoke tube.Data masking algorithms of the invention automatically identify datafrom regions that are obstructed and regions that lie outside a zone ofinterest based on spectral data obtained from those regions. In oneembodiment, the overlay identifies indeterminate regions withoutobscuring corresponding portions of the reference image, when viewed asa composite. Similarly, necrotic regions may be indicated on theoverlay, according to results of necrotic data masking algorithms of theinvention.

Systems of the invention allow performing fast and accurate image andspectral scans of tissue, such that both image and spectral data areobtained from each of a plurality of regions of the tissue sample. Eachdata point is keyed to its respective region, and the data are used todetermine tissue-class probabilities for regions of interest, as well asto identify indeterminate regions. These systems allow real-time displayof diagnostic results during a patient examination. For example, datamay be obtained from an in vivo tissue sample, and results of a tissueclassification algorithm may be displayed either during or immediatelyfollowing the examination. This provides a medical professional withnearly instantaneous, feedback which may be quickly comprehended andused for continued or follow-up examination. In some cases, the displayis prepared within seconds of obtaining data from the tissue. In othercases, the display is ready within a matter of minutes or a matter ofone or more hours after obtaining data from the tissue.

Accordingly, the invention comprises providing tissue-classprobabilities corresponding to regions of a tissue sample, creating anoverlay that uses color to key the probability values to thecorresponding regions, and displaying a composite of a reference imageof the tissue sample with the overlay. Methods of the inventionpreferentially include color-blending and/or filtering techniquesdesigned to convey diagnostically-relevant data in a manner commensuratewith the relevance of the data. In one embodiment, methods of theinvention are performed such that diagnostic results are displayed inreal-time during a patient examination. The step of providingtissue-class probabilities may comprise actual determination ofdiagnostic results according to methods of the invention. Alternatively,simply supplying probability values obtained using any tissueclassification method may encompass the providing step according to theinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects and features of the invention can be better understood withreference to the drawings described below, and the claims. The drawingsare not necessarily to scale, emphasis instead generally being placedupon illustrating the principles of the invention. In the drawings, likenumerals are used to indicate like parts throughout the various views.The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the U.S. Patent and TrademarkOffice upon request and payment of the necessary fee.

While the invention is particularly shown and described herein withreference to specific examples and specific embodiments, it should beunderstood by those skilled in the art that various changes in form anddetail may be made therein without departing from the spirit and scopeof the invention.

FIG. 1 is a block diagram featuring components of a tissuecharacterization system according to an illustrative embodiment of theinvention.

FIG. 2 is a schematic representation of components of the instrumentused in the tissue characterization system of FIG. 1 to obtain spectraldata and image data from a tissue sample according to an illustrativeembodiment of the invention.

FIG. 3 is a block diagram of the instrument used in the tissuecharacterization system of FIG. 1 according to an illustrativeembodiment of the invention.

FIG. 4 depicts a probe within a calibration port according to anillustrative embodiment of the invention.

FIG. 5 depicts an exemplary scan pattern used by the instrument of FIG.1 to obtain spatially-correlated spectral data and image data from atissue sample according to an illustrative embodiment of the invention.

FIG. 6 depicts front views of four exemplary arrangements ofillumination sources about a probe head according to variousillustrative embodiments of the invention.

FIG. 7 depicts exemplary illumination of a region of a tissue sampleusing light incident to the region at two different angles according toan illustrative embodiment of the invention.

FIG. 8 depicts illumination of a cervical tissue sample using a probeand a speculum according to an illustrative embodiment of the invention.

FIG. 9 is a schematic representation of an accessory device for a probemarked with identifying information in the form of a bar code accordingto an illustrative embodiment of the invention.

FIG. 10 is a block diagram featuring spectral data calibration andcorrection components of the tissue characterization system of FIG. 1according to an illustrative embodiment of the invention.

FIG. 11 is a block diagram featuring the spectral data pre-processingcomponent of the tissue characterization system of FIG. 1 according toan illustrative embodiment of the invention.

FIG. 12 shows a graph depicting reflectance spectral intensity as afunction of wavelength using an open air target according to anillustrative embodiment of the invention.

FIG. 13 shows a graph depicting reflectance spectral intensity as afunction of wavelength using a null target according to an illustrativeembodiment of the invention.

FIG. 14 shows a graph depicting fluorescence spectral intensity as afunction of wavelength using an open air target according to anillustrative embodiment of the invention.

FIG. 15 shows a graph depicting fluorescence spectral intensity as afunction of wavelength using a null target according to an illustrativeembodiment of the invention.

FIG. 16 is a representation of regions of a scan pattern and showsvalues of broadband reflectance intensity at each region using an openair target according to an illustrative embodiment of the invention.

FIG. 17 shows a graph depicting as a function of wavelength the ratio ofreflectance spectral intensity using an open air target to thereflectance spectral intensity using a null target according to anillustrative embodiment of the invention.

FIG. 18 shows a graph depicting as a function of wavelength the ratio offluorescence spectral intensity using an open air target to thefluorescence spectral intensity using a null target according to anillustrative embodiment of the invention.

FIG. 19 is a photograph of a customized target for factory/preventivemaintenance calibration and for pre-patient calibration of theinstrument used in the tissue characterization system of FIG. 1according to an illustrative embodiment of the invention.

FIG. 20 is a representation of the regions of the customized target ofFIG. 19 that are used to calibrate broadband reflectance spectral dataaccording to an illustrative embodiment of the invention.

FIG. 21 shows a graph depicting as a function of wavelength the meanreflectivity of the 10% diffuse target of FIG. 19 over the non-maskedregions shown in FIG. 20, measured using the same instrument on twodifferent days according to an illustrative embodiment of the invention.

FIG. 22A shows a graph depicting, for various individual instruments,curves of reflectance intensity (using the BB1 light source), eachinstrument curve representing a mean of reflectance intensity values forregions confirmed as metaplasia by impression and filtered according toan illustrative embodiment of the invention.

FIG. 22B shows a graph depicting, for various individual instruments,curves of reflectance intensity of the metaplasia-by-impression regionsof FIG. 22A, after adjustment according to an illustrative embodiment ofthe invention.

FIG. 23 shows a graph depicting the spectral irradiance of a NISTtraceable Quartz-Tungsten-Halogen lamp, along with a model of ablackbody emitter, used for determining an instrument responsecorrection for fluorescence intensity data according to an illustrativeembodiment of the invention.

FIG. 24 shows a graph depicting as a function of wavelength thefluorescence intensity of a dye solution at each region of a 499-pointscan pattern according to an illustrative embodiment of the invention.

FIG. 25 shows a graph depicting as a function of scan position thefluorescence intensity of a dye solution at a wavelength correspondingto a peak intensity seen in FIG. 24 according to an illustrativeembodiment of the invention.

FIG. 26 shows a graph depicting exemplary mean power spectra for variousindividual instruments subject to a noise performance criterionaccording to an illustrative embodiment of the invention.

FIG. 27A is a block diagram featuring steps an operator performs inrelation to a patient scan using the system of FIG. 1 according to anillustrative embodiment of the invention.

FIG. 27B is a block diagram featuring steps that the system of FIG. 1performs during acquisition of spectral data in a patient scan to detectand compensate for movement of the sample during the scan.

FIG. 28 is a block diagram showing the architecture of a video systemused in the system of FIG. 1 and how it relates to other components ofthe system of FIG. 1 according to an illustrative embodiment of theinvention.

FIG. 29A is a single video image of a target of 10% diffuse reflectivityupon which an arrangement of four laser spots is projected in a targetfocus validation procedure according to an illustrative embodiment ofthe invention.

FIG. 29B depicts the focusing image on the target in FIG. 29A withsuperimposed focus rings viewed by an operator through a viewfinderaccording to an illustrative embodiment of the invention.

FIG. 30 is a block diagram of a target focus validation procedureaccording to an illustrative embodiment of the invention.

FIG. 31 illustrates some of the steps of the target focus validationprocedure of FIG. 30 as applied to the target in FIG. 29A.

FIG. 32A represents the green channel of an RGB image of a cervicaltissue sample, used in a target focus validation procedure according toan illustrative embodiment of the invention.

FIG. 32B represents an image of the final verified laser spots on thecervical tissue sample of FIG. 32A, verified during application of thetarget focus validation procedure of FIG. 30 according to anillustrative embodiment of the invention.

FIG. 33 depicts a cervix model onto which laser spots are projectedduring an exemplary application of the target focus validation procedureof FIG. 30, where the cervix model is off-center such that the upper twolaser spots fall within the os region of the cervix model, according toan illustrative embodiment of the invention.

FIG. 34 shows a graph depicting, as a function of probe position, themean of a measure of focus of each of the four laser spots projectedonto the off-center cervix model of FIG. 33 in the target focusvalidation procedure of FIG. 30, according to an illustrative embodimentof the invention.

FIG. 35 shows a series of graphs depicting mean reflectance spectra forCIN 2/3 and non-CIN 2/3 tissues at a time prior to application of aceticacid, at a time corresponding to maximum whitening, and at a timecorresponding to the latest time at which data was obtained—used indetermining an optimal window for obtaining spectral data according toan illustrative embodiment of the invention.

FIG. 36 shows a graph depicting the reflectance discrimination functionspectra useful for differentiating between CIN 2/3 and non-CIN 2/3tissues, used in determining an optimal window for obtaining spectraldata according to an illustrative embodiment of the invention.

FIG. 37 shows a graph depicting the performance of two LDA (lineardiscriminant analysis) models as applied to reflectance data obtained atvarious times following application of acetic acid, used in determiningan optimal window for obtaining spectral data according to anillustrative embodiment of the invention.

FIG. 38 shows a series of graphs depicting mean fluorescence spectra forCIN 2/3 and non-CIN 2/3 tissues at a time prior to application of aceticacid, at a time corresponding to maximum whitening, and at a timecorresponding to the latest time at which data was obtained, used indetermining an optimal window for obtaining spectral data according toan illustrative embodiment of the invention.

FIG. 39 shows a graph depicting the fluorescence discrimination functionspectra useful for differentiating between CIN 2/3 and non-CIN 2/3tissues in determining an optimal window for obtaining spectral dataaccording to an illustrative embodiment of the invention.

FIG. 40 shows a graph depicting the performance of two LDA (lineardiscriminant analysis) models as applied to fluorescence data obtainedat various times following application of acetic acid, used indetermining an optimal window for obtaining spectral data according toan illustrative embodiment of the invention.

FIG. 41 shows a graph depicting the performance of three LDA models asapplied to data obtained at various times following application ofacetic acid, used in determining an optimal window for obtainingspectral data according to an illustrative embodiment of the invention.

FIG. 42 shows a graph depicting the determination of an optimal timewindow for obtaining diagnostic optical data using an optical amplitudetrigger, according to an illustrative embodiment of the invention.

FIG. 43 shows a graph depicting the determination of an optimal timewindow for obtaining diagnostic data using a rate of change of meanreflectance signal trigger, according to an illustrative embodiment ofthe invention.

FIG. 44A represents a 480×500 pixel image from a sequence of images ofin vivo human cervix tissue and shows a 256×256 pixel portion of theimage from which data is used in determining a correction for amisalignment between two images from a sequence of images of the tissuein the tissue characterization system of FIG. 1, according to anillustrative embodiment of the invention.

FIG. 44B depicts the image represented in FIG. 44A and shows a 128×128pixel portion of the image, made up of 16 individual 32×32 pixelvalidation cells, from which data is used in performing a validation ofthe misalignment correction determination according to an illustrativeembodiment of the invention.

FIG. 45 is a schematic flow diagram depicting steps in a method ofdetermining a correction for image misalignment in the tissuecharacterization system of FIG. 1, according to an illustrativeembodiment of the invention.

FIGS. 46A and 46B show a schematic flow diagram depicting steps in aversion of the method shown in FIG. 45 of determining a correction forimage misalignment according to an illustrative embodiment of theinvention.

FIGS. 47A and 47B show a schematic flow diagram depicting steps in aversion of the method shown in FIG. 45 of determining a correction forimage misalignment according to an illustrative embodiment of theinvention.

FIGS. 48A–F depict a subset of adjusted images from a sequence of imagesof a tissue with an overlay of gridlines showing the validation cellsused in validating the determinations of misalignment correction betweenthe images according to an illustrative embodiment of the invention.

FIG. 49A depicts a sample image after application of a 9-pixel size(9×9) Laplacian of Gaussian filter (LoG 9 filter) on an exemplary imagefrom a sequence of images of tissue, used in determining a correctionfor image misalignment, according to an illustrative embodiment of theinvention.

FIG. 49B depicts the application of both a feathering technique and aLaplacian of Gaussian filter on the exemplary image used in FIG. 49A toaccount for border processing effects, used in determining a correctionfor image misalignment according to an illustrative embodiment of theinvention.

FIG. 50A depicts a sample image after application of a LoG 9 filter onan exemplary image from a sequence of images of tissue, used indetermining a correction for image misalignment according to anillustrative embodiment of the invention.

FIG. 50B depicts the application of both a Hamming window technique anda LoG 9 filter on the exemplary image in FIG. 50A to account for borderprocessing effects in the determination of a correction for imagemisalignment according to an illustrative embodiment of the invention.

FIGS. 51A–F depict the determination of a correction for imagemisalignment using methods including the application of LoG filters ofvarious sizes, as well as the application of a Hamming window techniqueand a feathering technique according to illustrative embodiments of theinvention.

FIG. 52 shows a graph depicting exemplary mean values of reflectancespectral data as a function of wavelength for tissue regions affected byglare, tissue regions affected by shadow, and tissue regions affected byneither glare nor shadow according to an illustrative embodiment of theinvention.

FIG. 53 shows a graph depicting mean values and standard deviations ofbroadband reflectance spectral data using the BB1 channel light sourcefor regions confirmed as being obscured by blood, obscured by mucus,obscured by glare from the BB1 source, obscured by glare from the BB2source, or unobscured, according to an illustrative embodiment of theinvention.

FIG. 54 shows a graph depicting mean values and standard deviations ofbroadband reflectance spectral data using the BB2 channel light sourcefor regions confirmed as being obscured by blood, obscured by mucus,obscured by glare from the BB1 source, obscured by glare from the BB2source, or unobscured, according to an illustrative embodiment of theinvention.

FIG. 55 shows a graph depicting the weighted difference between the meanreflectance values of glare-obscured regions and unobscured regions oftissue as a function of wavelength used in determining metrics forapplication in the arbitration step in FIG. 1, according to anillustrative embodiment of the invention.

FIG. 56 shows a graph depicting the weighted difference between the meanreflectance values of blood-obscured regions and unobscured regions oftissue as a function of wavelength used in determining metrics forapplication in the arbitration step in FIG. 1, according to anillustrative embodiment of the invention.

FIG. 57 shows a graph depicting the weighted difference between the meanreflectance values of mucus-obscured regions and unobscured regions oftissue as a function of wavelength, used in determining metrics forapplication in the arbitration step in FIG. 1 according to anillustrative embodiment of the invention.

FIG. 58 shows a graph depicting a ratio of the weighted differencesbetween the mean reflectance values of glare-obscured regions andunobscured regions of tissue at two wavelengths, used in determiningmetrics for application in the arbitration step in FIG. 1 according toan illustrative embodiment of the invention.

FIG. 59 shows a graph depicting a ratio of the weighted differencesbetween the mean reflectance values of blood-obscured regions andunobscured regions of tissue at two wavelengths, used in determiningmetrics for application in the arbitration step in FIG. 1 according toan illustrative embodiment of the invention.

FIG. 60 shows a graph depicting a ratio of the weighted differencesbetween the mean reflectance values of mucus-obscured regions andunobscured regions of tissue at two wavelengths, used in determiningmetrics for application in the arbitration step in FIG. 1 according toan illustrative embodiment of the invention.

FIG. 61 shows a graph depicting as a function of wavelength mean valuesand confidence intervals of a ratio of BB1 and BB2 broadband reflectancespectral values for regions confirmed as being either glare-obscured orshadow-obscured tissue, used in determining metrics for application inthe arbitration step in FIG. 1 according to an illustrative embodimentof the invention.

FIG. 62 shows a graph depicting BB1 and BB2 broadband reflectancespectral data for a region of tissue where the BB1 data is affected byglare but the BB2 data is not, according to an illustrative embodimentof the invention.

FIG. 63 shows a graph depicting BB1 and BB2 broadband reflectancespectral data for a region of tissue where the BB2 data is affected byshadow but the BB1 data is not, according to an illustrative embodimentof the invention.

FIG. 64 shows a graph depicting BB1 and BB2 broadband reflectancespectral data for a region of tissue that is obscured by blood,according to an illustrative embodiment of the invention.

FIG. 65 shows a graph depicting BB1 and BB2 broadband reflectancespectral data for a region of tissue that is unobscured, according to anillustrative embodiment of the invention.

FIG. 66 shows a graph depicting the reduction in the variability ofbroadband reflectance measurements of CIN 2/3-confirmed tissue producedby applying the metrics in the arbitration step 128 of FIG. 1 to removedata affected by an artifact, according to an illustrative embodiment ofthe invention.

FIG. 67 shows a graph depicting the reduction in the variability ofbroadband reflectance measurements of tissue classified as “no evidenceof disease confirmed by pathology” produced by applying the metrics inthe arbitration step 128 of FIG. 1 to remove data affected by anartifact, according to an illustrative embodiment of the invention.

FIG. 68 shows a graph depicting the reduction in the variability ofbroadband reflectance measurements of tissue classified as “metaplasiaby impression” produced by applying the metrics in the arbitration step128 of FIG. 1 to remove data affected by an artifact, according to anillustrative embodiment of the invention.

FIG. 69 shows a graph depicting the reduction in the variability ofbroadband reflectance measurements of tissue classified as “normal byimpression” produced by applying the metrics in the arbitration step 128of FIG. 1 to remove data affected by an artifact, according to anillustrative embodiment of the invention.

FIG. 70A depicts an exemplary image of cervical tissue divided intoregions for which two types of reflectance spectral data and one type offluorescence spectral data are obtained, according to an illustrativeembodiment of the invention.

FIG. 70B is a representation of the regions depicted in FIG. 70A andshows the categorization of each region using the metrics in thearbitration step 128 of FIG. 1, according to an illustrative embodimentof the invention.

FIG. 71A depicts an exemplary image of cervical tissue divided intoregions for which two types of reflectance spectral data and one type offluorescence spectral data are obtained, according to an illustrativeembodiment of the invention.

FIG. 71B is a representation of the regions depicted in FIG. 71A andshows the categorization of each region using the metrics in thearbitration step 128 of FIG. 1, according to an illustrative embodimentof the invention.

FIG. 72A depicts an exemplary image of cervical tissue divided intoregions for which two types of reflectance spectral data and one type offluorescence spectral data are obtained, according to an illustrativeembodiment of the invention.

FIG. 72B is a representation of the regions depicted in FIG. 72A andshows the categorization of each region using the metrics in thearbitration step 128 of FIG. 1, according to an illustrative embodimentof the invention.

FIG. 73 is a block diagram depicting steps in a method of processing andcombining spectral data and image data obtained in the tissuecharacterization system of FIG. 1 to determine states of health ofregions of a tissue sample, according to an illustrative embodiment ofthe invention.

FIG. 74 is a block diagram depicting steps in the method of FIG. 73 infurther detail, according to an illustrative embodiment of theinvention.

FIG. 75 shows a scatter plot depicting discrimination between regions ofnormal squamous tissue and CIN 2/3 tissue for known reference data,obtained by comparing fluorescence intensity at about 460 nm to a ratioof fluorescence intensities at about 505 nm and about 410 nm, used indetermining an NED spectral mask (NED_(spec)) according to anillustrative embodiment of the invention.

FIG. 76 shows a graph depicting as a function of wavelength meanbroadband reflectance values for known normal squamous tissue regionsand known CIN 2/3 tissue regions, used in determining an NED spectralmask (NED_(spec)) according to an illustrative embodiment of theinvention.

FIG. 77 shows a graph depicting as a function of wavelength meanfluorescence intensity values for known squamous tissue regions andknown CIN 2/3 tissue regions, used in determining an NED spectral mask(NED_(spec)) according to an illustrative embodiment of the invention.

FIG. 78 shows a graph depicting values of a discrimination functionusing a range of numerator wavelengths and denominator wavelengths inthe discrimination analysis between known normal squamous tissue regionsand known CIN 2/3 tissue regions, used in determining an NED spectralmask (NED_(spec)) according to an illustrative embodiment of theinvention.

FIG. 79A depicts an exemplary reference image of cervical tissue from apatient scan in which spectral data is used in arbitration, NED spectralmasking, and statistical classification of interrogation points of thetissue sample, according to an illustrative embodiment of the invention.

FIG. 79B is a representation (obgram) of the interrogation points(regions) of the tissue sample depicted in FIG. 79A and shows pointsclassified as “filtered” following arbitration, “masked” following NEDspectral masking with two different sets of parameters, and “CIN 2/3”following statistical classification, according to an illustrativeembodiment of the invention.

FIG. 79C is a representation (obgram) of the interrogation points(regions) of the tissue sample depicted in FIG. 79A and shows pointsclassified as “filtered” following arbitration, “masked” following NEDspectral masking with two different sets of parameters, and “CIN 2/3”following statistical classification, according to an illustrativeembodiment of the invention.

FIG. 79D is a representation (obgram) of the interrogation points(regions) of the tissue sample depicted in FIG. 79A and shows pointsclassified as “filtered” following arbitration, “masked” following NEDspectral masking with two different sets of parameters, and “CIN 2/3”following statistical classification, according to an illustrativeembodiment of the invention.

FIG. 80 shows a graph depicting fluorescence intensity as a function ofwavelength from an interrogation point confirmed as invasive carcinomaby pathology and necrotic tissue by impression, used in determining aNecrosis spectral mask according to an illustrative embodiment of theinvention.

FIG. 81 shows a graph depicting broadband reflectance BB1 and BB2 asfunctions of wavelength from an interrogation point confirmed asinvasive carcinoma by pathology and necrotic tissue by impression, usedin determining a Necrosis spectral mask according to an illustrativeembodiment of the invention.

FIG. 82A depicts an exemplary reference image of cervical tissue fromthe scan of a patient confirmed as having advanced invasive cancer inwhich spectral data is used in arbitration, Necrosis spectral masking,and statistical classification of interrogation points of the tissuesample, according to an illustrative embodiment of the invention.

FIG. 82B is a representation (obgram) of the interrogation points(regions) of the tissue sample depicted in FIG. 82A and shows pointsclassified as “filtered” following arbitration, “masked” followingapplication of the “Porphyrin” and “FAD” portions of the Necrosisspectral mask, and “CIN 2/3” following statistical classification,according to an illustrative embodiment of the invention.

FIG. 83 shows a graph depicting as a function of wavelength meanbroadband reflectance values for known cervical edge regions and knownCIN 2/3 tissue regions, used in a discrimination analysis to determine acervical edge/vaginal wall ([CE]_(spec)) spectral mask according to anillustrative embodiment of the invention.

FIG. 84 shows a graph depicting as a function of wavelength meanfluorescence intensity values for known cervical edge regions and knownCIN 2/3 tissue regions, used in a discrimination analysis to determine acervical edge/vaginal wall ([CE]_(spec)) spectral mask according to anillustrative embodiment of the invention.

FIG. 85 shows a graph depicting as a function of wavelength meanbroadband reflectance values for known vaginal wall regions and knownCIN 2/3 tissue regions, used in a discrimination analysis to determine acervical edge/vaginal wall ([CE]_(spec)) spectral mask according to anillustrative embodiment of the invention.

FIG. 86 shows a graph depicting as a function of wavelength meanfluorescence intensity values for known vaginal wall regions and knownCIN 2/3 tissue regions, used in a discrimination analysis to determine acervical edge/vaginal wall ([CE]_(spec)) spectral mask according to anillustrative embodiment of the invention.

FIG. 87A depicts an exemplary reference image of cervical tissue from apatient scan in which spectral data is used in arbitration and cervicaledge/vaginal wall ([CE]_(spec)) spectral masking, according to anillustrative embodiment of the invention.

FIG. 87B is a representation (obgram) of the interrogation points(regions) of the tissue sample depicted in FIG. 87A and shows pointsclassified as “filtered” following arbitration and “masked” followingcervical edge/vaginal wall ([CE]_(spec)) spectral masking, according toan illustrative embodiment of the invention.

FIG. 88 shows a graph depicting as a function of wavelength meanbroadband reflectance values for known pooling fluids regions and knownCIN 2/3 tissue regions, used in a discrimination analysis to determine afluids/mucus ([MU]_(spec)) spectral mask according to an illustrativeembodiment of the invention.

FIG. 89 shows a graph depicting as a function of wavelength meanfluorescence intensity values for known pooling fluids regions and knownCIN 2/3 tissue regions, used in a discrimination analysis to determine afluids/mucus ([MU]_(spec)) spectral mask according to an illustrativeembodiment of the invention.

FIG. 90 shows a graph depicting as a function of wavelength meanbroadband reflectance values for known mucus regions and known CIN 2/3tissue regions, used in a discrimination analysis to determine afluids/mucus ([MU]_(spec)) spectral mask according to an illustrativeembodiment of the invention.

FIG. 91 shows a graph depicting as a function of wavelength meanfluorescence intensity values for known mucus regions and known CIN 2/3tissue regions, used in a discrimination analysis to determine afluids/mucus ([MU]_(spec)) spectral mask according to an illustrativeembodiment of the invention.

FIG. 92A depicts an exemplary reference image of cervical tissue from apatient scan in which spectral data is used in arbitration andfluids/mucus ([MU]_(spec)) spectral masking, according to anillustrative embodiment of the invention.

FIG. 92B is a representation (obgram) of the interrogation points(regions) of the tissue sample depicted in FIG. 92A and shows pointsclassified as “filtered” following arbitration and “masked” followingfluids/mucus ([MU]_(spec)) spectral masking, according to anillustrative embodiment of the invention.

FIG. 93 depicts image masks determined from an image of a tissue sampleand shows how the image masks are combined with respect to each spectralinterrogation point (region) of the tissue sample, according to anillustrative embodiment of the invention.

FIG. 94A depicts an exemplary image of cervical tissue obtained during apatient examination and used in determining a corresponding glare imagemask, Glare_(vid), according to an illustrative embodiment of theinvention.

FIG. 94B represents a glare image mask, Glare_(vid), corresponding tothe exemplary image in FIG. 94A, according to an illustrative embodimentof the invention.

FIG. 95 is a block diagram depicting steps in a method of determining aglare image mask, Glare_(vid), for an image of cervical tissue,according to an illustrative embodiment of the invention.

FIG. 96 shows a detail of a histogram used in a method of determining aglare image mask, Glare_(vid), for an image of cervical tissue,according to an illustrative embodiment of the invention.

FIG. 97A depicts an exemplary image of cervical tissue obtained during apatient examination and used in determining a correspondingregion-of-interest image mask, [ROI]_(vid), according to an illustrativeembodiment of the invention.

FIG. 97B represents a region-of-interest image mask, [ROI]_(vid),corresponding to the exemplary image in FIG. 120A, according to anillustrative embodiment of the invention.

FIG. 98 is a block diagram depicting steps in a method of determining aregion-of-interest image mask, [ROI]_(vid), for an image of cervicaltissue, according to an illustrative embodiment of the invention.

FIG. 99A depicts an exemplary image of cervical tissue obtained during apatient examination and used in determining a corresponding smoke tubeimage mask, [ST]_(vid), according to an illustrative embodiment of theinvention.

FIG. 99B represents a smoke tube image mask, [ST]_(vid), correspondingto the exemplary image in FIG. 99A, according to an illustrativeembodiment of the invention.

FIG. 100 is a block diagram depicting steps in a method of determining asmoke tube image mask, [ST]_(vid), for an image of cervical tissue,according to an illustrative embodiment of the invention.

FIG. 101A depicts an exemplary image of cervical tissue obtained duringa patient examination and used in determining a corresponding os imagemask, Os_(vid), according to an illustrative embodiment of theinvention.

FIG. 101B represents an os image mask, Os_(vid), corresponding to theexemplary image in FIG. 101A, according to an illustrative embodiment ofthe invention.

FIG. 102 is a block diagram depicting steps in a method of determiningan os image mask, Os_(vid), for an image of cervical tissue, accordingto an illustrative embodiment of the invention.

FIG. 103A depicts an exemplary image of cervical tissue obtained duringa patient examination and used in determining a corresponding bloodimage mask, Blood_(vid), according to an illustrative embodiment of theinvention.

FIG. 103B represents a blood image mask, Blood_(vid), corresponding tothe exemplary image in FIG. 103A, according to an illustrativeembodiment of the invention.

FIG. 104 is a block diagram depicting steps in a method of determining ablood image mask, Blood_(vid), for an image of cervical tissue,according to an illustrative embodiment of the invention.

FIG. 105A depicts an exemplary image of cervical tissue obtained duringa patient examination and used in determining a corresponding mucusimage mask, Mucus_(vid), according to an illustrative embodiment of theinvention.

FIG. 105B represents a mucus image mask, Mucus_(vid), corresponding tothe exemplary reference image in FIG. 105A, according to an illustrativeembodiment of the invention.

FIG. 106 is a block diagram depicting steps in a method of determining amucus image mask, Mucus_(vid), for an image of cervical tissue,according to an illustrative embodiment of the invention.

FIG. 107A depicts an exemplary reference image of cervical tissueobtained during a patient examination and used in determining acorresponding speculum image mask, [SP]_(vid), according to anillustrative embodiment of the invention.

FIG. 107B represents a speculum image mask, [SP]_(vid), corresponding tothe exemplary image in FIG. 107A, according to an illustrativeembodiment of the invention.

FIG. 108 is a block diagram depicting steps in a method of determining aspeculum image mask, [SP]_(vid), for an image of cervical tissue,according to an illustrative embodiment of the invention.

FIG. 109A depicts an exemplary image of cervical tissue obtained duringa patient examination and used in determining a vaginal wall image mask,[VW]_(vid), according to an illustrative embodiment of the invention.

FIG. 109B represents the image of FIG. 109A overlaid with a vaginal wallimage mask, [VW]_(vid), following extension, determined according to anillustrative embodiment of the invention.

FIG. 110 is a block diagram depicting steps in a method of determining avaginal wall image mask, [VW]_(vid), for an image of cervical tissue,according to an illustrative embodiment of the invention.

FIG. 111A depicts an exemplary image of cervical tissue obtained duringa patient examination and used in determining a correspondingfluid-and-foam image mask, [FL]_(vid), according to an illustrativeembodiment of the invention.

FIG. 111B represents a fluid-and-foam image mask, [FL]_(vid),corresponding to the exemplary image in FIG. 111A, according to anillustrative embodiment of the invention.

FIG. 112 is a block diagram depicting steps in a method of determining afluid-and-foam image mask, [FL]_(vid), for an image of cervical tissue,according to an illustrative embodiment of the invention.

FIGS. 113A–C show graphs representing a step in a method of image visualenhancement in which a piecewise linear transformation of an input imageproduces an output image with enhanced image brightness and contrast,according to one embodiment of the invention.

FIG. 114A depicts an exemplary image of cervical tissue obtained duringa patient examination and used as a reference (base) image in a methodof disease probability display, according to one embodiment of theinvention.

FIG. 114B depicts the output overlay image corresponding to thereference image in FIG. 114A, produced using a method of diseaseprobability display according to one embodiment of the invention.

FIG. 115A represents a disease display layer produced in a method ofdisease probability display for the reference image in FIG. 114A,wherein CIN 2/3 probabilities at interrogation points are represented bycircles with intensities scaled by CIN 2/3 probability, according to oneembodiment of the invention.

FIG. 115B represents the disease display layer of FIG. 114B followingfiltering using a Hamming filter, according to one embodiment of theinvention.

FIG. 116 represents the color transformation used to determine thedisease display layer image in a disease probability display method,according to one embodiment of the invention.

FIG. 117A depicts an exemplary reference image of cervical tissue havingnecrotic regions, obtained during a patient examination and used as areference (base) image in a method of disease probability display,according to one embodiment of the invention.

FIG. 117B depicts the output overlay image corresponding to thereference image in FIG. 117A, including necrotic regions, indeterminateregions, and CIN 2/3 regions, and produced using a method of diseaseprobability display according to one embodiment of the invention.

DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENT

Table of Contents Page System overview 30 Instrument 35 Spectralcalibration 49 Patient scan procedure 97 Video calibration and focusing100 Determining optimal data acquisition window 112 Motion tracking 129Broadband reflectance arbitration and low-signal masking 156Classification system overview 179 Spectral masking 184 Image masking196 Glare_(vid) 202 [ROI]_(vid) 206 [ST]_(vid) 207 Os_(vid) 215Blood_(vid) 219 Mucus_(vid) 224 [SP]_(vid) 228 [VW]_(vid) 240 [FL]_(vid)253 Classifiers 262 Combining spectral and image data 273 Imageenhancement 282 Diagnostic display 288

The Table of Contents above is provided as a general organizationalguide to the Description of the Illustrative Embodiment. Entries in theTable do not serve to limit support for any given element of theinvention to a particular section of the Description.

System 100 Overview

The invention provides systems and methods for obtaining spectral dataand image data from a tissue sample, for processing the data, and forusing the data to diagnose the tissue sample. As used herein, “spectraldata” from a tissue sample includes data corresponding to any wavelengthof the electromagnetic spectrum, not just the visible spectrum. Whereexact wavelengths are specified, alternate embodiments comprise usingwavelengths within a ±5 nm range of the given value, within a ±10 nmrange of the given value, and within a ±25 nm range of the given value.As used herein, “image data” from a tissue sample includes data from avisual representation, such as a photo, a video frame, streaming video,and/or an electronic, digital or mathematical analogue of a photo, videoframe, or streaming video. As used herein, a “tissue sample” maycomprise, for example, animal tissue, human tissue, living tissue,and/or dead tissue. A tissue sample may be in vivo, in situ, ex vivo, orex situ, for example. A tissue sample may comprise material in thevacinity of tissue, such as non-biological materials includingdressings, chemical agents, and/or medical instruments, for example.

Embodiments of the invention include obtaining data from a tissuesample, determining which data are of diagnostic value, processing theuseful data to obtain a prediction of disease state, and displaying theresults in a meaningful way. In one embodiment, spectral data and imagedata are obtained from a tissue sample and are used to create adiagnostic map of the tissue sample showing regions in which there is ahigh probability of disease.

The systems and methods of the invention can be used to perform anexamination of in situ tissue without the need for excision or biopsy.In an illustrative embodiment, the systems and methods are used toperform in-situ examination of the cervical tissue of a patient in anon-surgical setting, such as in a doctor's office or examination room.The examination may be preceded or accompanied by a routine pap smearand/or colposcopic examination, and may be followed-up by treatment orbiopsy of suspect tissue regions.

FIG. 1 depicts a block diagram featuring components of a tissuecharacterization system 100 according to an illustrative embodiment ofthe invention. Each component of the system 100 is discussed in moredetail herein. The system includes components for acquiring data,processing data, calculating disease probabilities, and displayingresults.

In the illustrative system 100 of FIG. 1, an instrument 102 obtainsspectral data and image data from a tissue sample. The instrument 102obtains spectral data from each of a plurality of regions of the sampleduring a spectroscopic scan of the tissue 104. During a scan, videoimages of the tissue are also obtained by the instrument 102.Illustratively, one or more complete spectroscopic spectra are obtainedfor each of 500 discrete regions of a tissue sample during a scanlasting about 12 seconds. However, in other illustrative embodiments anynumber of discrete regions may be scanned and the duration of each scanmay vary. Since in-situ tissue may shift due to involuntary or voluntarypatient movement during a scan, video images are used to detect shiftsof the tissue, and to account for the shifts in the diagnostic analysisof the tissue. Preferably, a detected shift is compensated for in realtime 106. For example, as described below in further detail, one or morecomponents of the instrument 102 may be automatically adjusted duringthe examination of a patient while spectral data are obtained in orderto compensate for a detected shift caused by patient movement.Additionally or alternatively, the real-time tracker 106 provides acorrection for patient movement that is used to process the spectraldata before calculating disease probabilities. In addition to usingimage data to track movement, the illustrative system 100 of FIG. 1 usesimage data to identify regions that are obstructed or are outside theareas of interest of a tissue sample 108. This feature of the system 100of FIG. 1 is discussed herein in more detail.

The system 100 shown in FIG. 1 includes components for performingfactory tests and periodic preventive maintenance procedures 110, theresults of which 112 are used to preprocess patient spectral data 114.In addition, reference spectral calibration data are obtained 116 in anexamination setting prior to each patient examination, and the results118 of the pre-patient calibration are used along with the factory andpreventive maintenance results 112 to preprocess patient spectral data114.

The instrument 102 of FIG. 1 includes a frame grabber 120 for obtaininga video image of the tissue sample. A focusing method 122 is applied andvideo calibration is performed 124. The corrected video data may then beused to compensate for patient movement during the spectroscopic dataacquisition 104. The corrected video data is also used in image masking108, which includes identifying obstructed regions of the tissue sample,as well as regions of tissue that lie outside an area of diagnosticinterest. In one illustrative embodiment, during a patient scan, asingle image is used to compute image masks 108 and to determine abrightness and contrast correction 126 for displaying diagnosticresults. In illustrative alternative embodiments, more than one image isused to create image masks and/or to determine a visual displaycorrection.

In the system of FIG. 1, spectral data are acquired 104 within apredetermined period of time following the application of a contrastagent, such as acetic acid, to the tissue sample. According to theillustrative embodiment, four raw spectra are obtained for each ofapproximately 500 regions of the tissue sample and are processed. Afluorescence spectrum, two broadband reflectance (backscatter) spectra,and a reference spectrum are obtained at each of the regions over arange from about 360 nm to about 720 nm wavelength. The period of timewithin which a scan is acquired is chosen so that the accuracy of theresulting diagnosis is maximized. In one illustrative embodiment, aspectral data scan of a cervical tissue sample is performed over anapproximately 12-second period of time within a range between about 30seconds and about 130 seconds following application of acetic acid tothe tissue sample.

The illustrative system 100 includes data processing components foridentifying data that are potentially non-representative of the tissuesample. Preferably, potentially non-representative data are eitherhard-masked or soft-masked. Hard-masking of data includes eliminatingthe identified, potentially non-representative data from furtherconsideration. This results in an indeterminate diagnosis in thecorresponding region. Hard masks are determined in components 128, 130,and 108 of the system 100. Soft masking includes applying a weightingfunction or weighting factor to the identified, potentiallynon-representative data. The weighting is taken into account duringcalculation of disease probability 132, and may or may not result in anindeterminate diagnosis in the corresponding region. Soft masks aredetermined in component 130 of the system 100.

Soft masking provides a means of weighting spectral data according tothe likelihood that the data is representative of clear, unobstructedtissue in a region of interest. For example, if the system 100determines there is a possibility that one kind of data from a givenregion is affected by an obstruction, such as blood or mucus, that datais “penalized” by attributing a reduced weighting to that data duringcalculation of disease probability 132. Another kind of data from thesame region that is determined by the system 100 not to be affected bythe obstruction is more heavily weighted in the diagnostic step than thepossibly-affected data, since the unaffected data is attributed agreater weighting in the calculation of disease probability 132.

In the illustrative system 100, soft masking is performed in addition toarbitration of two or more redundant data sets. Arbitration of data setsis performed in component 128. In the illustrative embodiment, this typeof arbitration employs the following steps: obtaining two sets ofbroadband reflectance (backscatter) data from each region of the tissuesample using light incident to the region at two different angles;determining if one of the data sets is affected by an artifact such asshadow, glare, or obstruction; eliminating one of the redundantreflectance data sets so affected; and using the other data set in thediagnosis of the tissue at the region. If both of the data sets areunaffected by an artifact, a mean of the two sets is used.

According to the illustrative embodiment, the instrument 102 obtainsboth video images and spectral data from a tissue sample. The spectraldata may include fluorescence data and broadband reflectance(backscatter) data. The raw spectral data are processed and then used ina diagnostic algorithm to determine disease probability for regions ofthe tissue sample. According to the illustrative embodiment, both imagedata and spectral data are used to mask data that is potentiallynon-representative of unobstructed regions of interest of the tissue. Inanother illustrative embodiment, both the image data and the spectraldata are alternatively or additionally used in the diagnostic algorithm.

The system 100 also includes a component 132 for determining a diseaseprobability at each of a plurality of the approximately 500interrogation points using spectral data processed in the components 128and 130 and using the image masks determined in component 108.Illustratively, the disease probability component 132 processes spectraldata with statistical and/or heuristics-based(non-statistically-derived) spectral classifiers 134, incorporates imageand/or spectral mask information 136, and assigns a probability of highgrade disease, such as CIN 2+, to each examined region of the tissuesample. The classifiers use stored, accumulated training data fromsamples of known disease state. The disease display component 138graphically presents regions of the tissue sample having the highestprobability of high grade disease by employing a color map overlay ofthe cervical tissue sample. The disease display component 138 alsodisplays regions of the tissue that are necrotic and/or regions at whicha disease probability could not be determined.

Each of the components of the illustrative system 100 is described inmore detail below.

Instrument—102

FIG. 2 is a schematic representation of components of the instrument 102used in the tissue characterization system 100 of FIG. 1 to obtainspectral data and image data from a tissue sample according to anillustrative embodiment of the invention. The instrument of FIG. 2includes a console 140 connected to a probe 142 by way of a cable 144.The cable 144 carries electrical and optical signals between the console140 and the probe 142. In an alternative embodiment, signals aretransmitted between the console 140 and the probe 142 wirelessly,obviating the need for the cable 144. The probe 142 accommodates adisposable component 146 that comes into contact with tissue and may bediscarded after one use. The console 140 and the probe 142 aremechanically connected by an articulating arm 148, which can alsosupport the cable 144. The console 140 contains much of the hardware andthe software of the system, and the probe 142 contains the necessaryhardware for making suitable spectroscopic observations. The details ofthe instrument 100 are further explained in conjunction with FIG. 3.

FIG. 3 shows an exemplary operational block diagram 150 of an instrument102 of the type depicted in FIG. 2. Referring to FIGS. 1 and 2, in someillustrative embodiments the instrument 102 includes features ofsingle-beam spectrometer devices, but is adapted to include otherfeatures of the invention. In other illustrative embodiments, theinstrument 102 is substantially the same as double-beam spectrometerdevices, adapted to include other features of the invention. In stillother illustrative embodiments the instrument 102 employs other types ofspectroscopic devices. In the depicted embodiment, the console 140includes a computer 152, which executes software that controls theoperation of the instrument 102. The software includes one or moremodules recorded on machine-readable media such as magnetic disks,magnetic tape, CD-ROM, and semiconductor memory, for example.Preferably, the machine-readable medium is resident within the computer152. In alternative embodiments, the machine-readable medium can beconnected to the computer 152 by a communication link. However, inalternative embodiments, one can substitute computer instructions in theform of hardwired logic for software, or one can substitute firmware(i.e., computer instructions recorded on devices such as PROMs, EPROMS,EEPROMs, or the like) for software. The term machine-readableinstructions as used herein is intended to encompass software, hardwiredlogic, firmware, object code and the like.

The computer 152 of the instrument 102 is preferably a general purposecomputer. The computer 152 can be, for example, an embedded computer, apersonal computer such as a laptop or desktop computer, or another typeof computer, that is capable of running the software, issuing suitablecontrol commands, and recording information in real-time. Theillustrative computer 152 includes a display 154 for reportinginformation to an operator of the instrument 102, a keyboard 156 forenabling the operator to enter information and commands, and a printer158 for providing a print-out, or permanent record, of measurements madeby the instrument 102 and for printing diagnostic results, for example,for inclusion in the chart of a patient. According to the illustrativeembodiment of the invention, some commands entered at the keyboard 156enable a user to perform certain data processing tasks, such asselecting a particular spectrum for analysis, rejecting a spectrum,and/or selecting particular segments of a spectrum for normalization.Other commands enable a user to select the wavelength range for eachparticular segment and/or to specify both wavelength contiguous andnon-contiguous segments. In one illustrative embodiment, dataacquisition and data processing are automated and require little or nouser input after initializing a scan.

The illustrative console 140 also includes an ultraviolet (UV) source160 such as a nitrogen laser or a frequency-tripled Nd:YAG laser, one ormore white light sources 162 such as one, two, three, four, or moreXenon flash lamps, and control electronics 164 for controlling the lightsources both as to intensity and as to the time of onset of operationand the duration of operation. One or more power supplies 166 areincluded in the illustrative console 140 to provide regulated power forthe operation of all of the components of the instrument 102. Theillustrative console 140 of FIG. 3 also includes at least onespectrometer and at least one detector (spectrometer and detector 168)suitable for use with each of the light sources. In some illustrativeembodiments, a single spectrometer operates with both the UV lightsource 160 and the white light source(s) 162. The same detector mayrecord both UV and white light signals. However, in other illustrativeembodiments, different detectors are used for each light source.

The illustrative console 140 further includes coupling optics 170 tocouple the UV illumination from the UV light source 160 to one or moreoptical fibers in the cable 144 for transmission to the probe 142, andcoupling optics 172 for coupling the white light illumination from thewhite light source(s) 162 to one or more optical fibers in the cable 144for transmission to the probe 142. The spectral response of a specimento UV illumination from the UV light source 160 observed by the probe142 is carried by one or more optical fibers in the cable 144 fortransmission to the spectrometer and detector 168 in the console 140.The spectral response of a specimen to the white light illumination fromthe white light source(s) 162 observed by the probe 142 is carried byone or more optical fibers in the cable 144 for transmission to thespectrometer and detector 168 in the console 140. As shown in FIG. 3,the console 140 includes a footswitch 174 to enable an operator of theinstrument 102 to signal when it is appropriate to commence a spectralscan by stepping on the switch. In this manner, the operator has his orher hands free to perform other tasks, for example, aligning the probe142.

The console 140 additionally includes a calibration port 176 into whicha calibration target may be placed for calibrating the opticalcomponents of the instrument 102. Illustratively, an operator places theprobe 142 in registry with the calibration port 176 and issues a commandthat starts the calibration operation. In illustrative calibrationoperation, a calibrated light source provides a calibration signal inthe form of an illumination of known intensity over a range ofwavelengths, and/or at a number of discrete wavelengths. The probe 142detects the calibration signal, and transmits the detected signalthrough the optical fiber in the cable 144 to the spectrometer anddetector 168. A test spectral result is obtained. A calibration of thespectral system can be computed as the ratio of the amplitude of theknown illumination at a particular wavelength divided by the testspectral result at the same wavelength. Calibration may include factorycalibration 110, preventive maintenance calibration 110, and/orpre-patient calibration 116, as shown in the system 100 of FIG. 1.Pre-patient calibration 116 may be performed to account forpatient-to-patient variation, for example.

FIG. 4 depicts the illustrative probe 142 of FIG. 2 resting within acalibration port 176 according to an illustrative embodiment of theinvention. Referring to FIGS. 2–4, the illustrative calibration port 176is adjustably attached to the probe 142 or the console 140 to allow anoperator to perform pre-patient calibration without assemblingdetachable parts. The pre-patient calibration port may contain one ormore pre-positioned calibration targets, such as a customized target 426(see also FIG. 19) and a null target 187, both described in more detailbelow.

According to the illustrative embodiment, factory and/or preventivemaintenance calibration includes using a portable, detachablecalibration port to calibrate any number of individual units, allowingfor a standardized calibration procedure among various instruments.Preferably, the calibration port 176 is designed to prevent stray roomlight or other external light from affecting a calibration measurementwhen a calibration target is in place in the calibration port 176. Forexample, as shown in FIG. 4, the null target 187 can be positioned upagainst the probe head 192 by way of an actuator 189 such that theeffect of external stray light is minimized. When not in use, the nulltarget 187 is positioned out of the path of light between the customizedtarget 426 and the collection optics 200, as depicted in FIG. 4. Anadditional fitting may be placed over the probe head 192 to furtherreduce the effect of external stray light. According to one illustrativeembodiment, the target 187 in the calibration port 176 is locatedapproximately 100 mm from the probe head 192; and the distance lighttravels from the target 187 to the first optical component of the probe142 is approximately 130 mm. The location of the target (in relation tothe probe head 192) during calibration may approximate the location oftissue during a patient scan.

The illustrative probe 142 includes probe optics 178 for illuminating aspecimen to be analyzed with UV light from the UV source 160 and forcollecting the fluorescent and broadband reflectance (backscatter)illumination from the specimen being analyzed. The illustrative probe142 of FIGS. 2 and 3 includes a scanner assembly 180 that providesillumination from the UV source 160, for example, in a raster patternover a target area of the specimen of cervical tissue to be analyzed.The probe 142 also includes a video camera 182 for observing andrecording visual images of the specimen under analysis. The probe 142also includes a targeting source 184 for determining where on thesurface of the specimen to be analyzed the probe 142 is pointing. Theprobe 142 also includes white light optics 186 to deliver white lightfrom the white light source(s) 162 for recording the reflectance dataand to assist the operator in visualizing the specimen to be analyzed.Once the operator aligns the instrument 102 and depresses the footswitch174, the computer 152 controls the actions of the light sources 160,162, the coupling optics 170, 112, the transmission of light signals andelectrical signals through the cable 144, the operation of the probeoptics 178 and the scanner assembly 180, the retrieval of observedspectra, the coupling of the observed spectra into the spectrometer anddetector 168 via the cable 144, the operation of the spectrometer anddetector 168, and the subsequent signal processing and analysis of therecorded spectra.

FIG. 4 depicts the probe 142 having top and bottom illumination sources188, 190 according to an illustrative embodiment of the invention. Inthis embodiment, the illumination sources 188, 190 are situated at anupper and a lower location about the perimeter of a probe head 192 suchthat there is illuminating light incident to a target area at each oftwo different angles. In one embodiment, the target area is a tissuesample. The probe head 192 contains probe optics 178 for illuminatingregions of tissue and for collecting illumination reflected or otherwiseemitted from regions of tissue. Illustratively, the probe optics forcollecting the illumination 200 are located between the top and bottomillumination sources 188, 190. In other illustrative embodiments, otherarrangements of the illuminating and collecting probe optics 178 areused that allow the illumination of a given region of tissue with lightincident to the region at more than one angle. One such arrangementincludes the collecting optics 200 positioned around the illuminatingoptics.

In one illustrative embodiment, the top and bottom illumination sources188, 190 are alternately turned on and off in order to sequentiallyilluminate the tissue at equal and opposite angles relative to thecollection axis. For example, the top illumination source 188 is turnedon while the bottom illumination source 190 is turned off, such thatspectral measurements may be obtained for light reflected from a regionof the tissue sample 194 illuminated with light incident to the regionat a first angle. This angle is relative to the surface of the tissuesample at a point on the region, for example. Then, the top illuminationsource 188 is turned off while the bottom illumination source 190 isturned on, such that spectral measurements may be obtained using lightincident to the region at a second angle. If data obtained using one ofthe illumination sources is adversely affected by an artifact, such asglare or shadow, then data obtained using another illumination source,with light incident to the region at a different angle, may beunaffected by the artifact and may still be useful. The spectralmeasurements can include reflectance and/or fluorescence data obtainedover a range of wavelengths.

According to the various illustrative embodiments, the top and thebottom illumination sources 188, 190 may be alternately cycled on andoff more than once while obtaining data for a given region. Also, cyclesof the illumination sources 188, 190 may overlap, such that more thanone illumination source is on at one time for at least part of theillumination collection procedure. Other illumination alternationschemes are possible, depending at least in part on the arrangement ofillumination sources 188, 190 in relation to the probe head 192.

After data are obtained from one region of the tissue using lightincident to the region at more than one angle, data may likewise beobtained from another region of the tissue. In the illustrativeembodiment of FIG. 4, the scanner assembly 180 illuminates a target areaof the tissue sample region-by-region. Illustratively, a first region isilluminated using light incident to the region at more than one angle asdescribed above, then the probe optics 178 are automatically adjusted torepeat the illumination sequence at a different region within the targetarea of the tissue sample. The illustrative process is repeated until adesired subset of the target area has been scanned. As mentioned above,preferably about five hundred regions are scanned within a target areahaving a diameter of about 25-mm. Using the instrument 102, the scan ofthe aforementioned five hundred regions takes about 12 seconds. In otherillustrative embodiments, the number of regions scanned, the size of thetarget area, and/or the duration of the scan vary from the above.

FIG. 5 depicts an exemplary scan pattern 202 used by the instrument 102to obtain spatially-correlated spectral data and image data from atissue sample according to an illustrative embodiment of the invention.Illustratively, spectral data are obtained at 499 regions of the tissuesample, plus one region out of the field of view of the cervix obtained,for example, for calibration purposes. The exemplary scan pattern 202 ofFIG. 5 includes 499 regions 204 whose centers are inside a circle 206that measures about 25.8 mm in diameter. The center of each region isabout 1.1 mm away from each of the nearest surrounding regions. This maybe achieved by offsetting each scan line by about 0.9527 mm in they-direction and by staggering each scan line in the x-direction by about0.55 mm. Each of the 499 regions is about 0.7 mm in diameter. In otherillustrative embodiments, other geometries are used.

According to the illustrative embodiment, the spectral data acquisitioncomponent 104 of the system 100 depicted in FIG. 1 is performed usingthe scan pattern 202 shown in FIG. 5. A fluorescence spectrum, twobroadband reflectance spectra, and a reference spectrum are obtained ateach region 204. The two broadband reflectance spectra use lightincident to the sample at two different angles. A scan preferably beginsat the center region 208, which corresponds to a pixel in a 500×480pixel video image of the tissue sample at location 250, 240. Asdiscussed in more detail below, a sequence of video images of the tissuesample may be taken during a scan of the 499 regions shown in FIG. 5 andmay be used to detect and compensate for movement of the tissue sampleduring the scan. The real-time tracker component 106 of the system 100shown in FIG. 1 performs this motion detection and compensationfunction. Preferably, the scanner assembly 180 of FIG. 3 includescontrols for keeping track of the data obtained, detecting a stalledscan process, aborting the scan if the tissue is exposed to temperatureor light outside of acceptable ranges, and/or monitoring and reportingerrors detected by the spectral data acquisition component 104 of thesystem of FIG. 1.

FIG. 6 depicts front views of four exemplary arrangements 210, 212, 214,216 of illumination sources about a probe head 192 according to variousillustrative embodiments of the invention. The drawings are not toscale; they serve to illustrate exemplary relative arrangements ofillumination sources about the perimeter of a probe head 192. Otherarrangements include positioning collecting optics 200 around theperimeter of the probe head 192, about the illumination sources, or inany other suitable location relative to the illumination sources. Thefirst arrangement 210 of FIG. 6 has one top illumination source 218 andone bottom illumination source 220, which are alternately cycled on andoff as described above. The illumination sources are arranged about thecollecting optics 200, which are located in the center of the probe head192. Light from an illumination source is reflected from the tissue andcaptured by the collecting optics 200.

The second arrangement 212 of FIG. 6 is similar to the first arrangement210, except that there are two illumination sources 222, 224 in the tophalf of the probe head 192 and two illumination sources 226, 228 in thebottom half of the probe head 192. In one embodiment, the two lightsabove the midline 230 are turned on and the two lights below the midline230 are turned off while obtaining a first set of spectral data; thenthe lights above the midline 230 are turned off and the lights below themidline 230 are turned on while obtaining a second set of spectral data.In an alternate illustrative embodiment, only one of the fourillumination sources are turned on at a time to obtain four sets ofspectral data for a given region. Other illustrative embodiments includeturning the illumination sources on and off in other patterns. Otheralternative embodiments include using noncircular or otherwisedifferently shaped illumination sources, and/or using a different numberof illumination sources.

The third arrangement 214 of FIG. 6 includes each illumination source232, 234 positioned on either side of the probe head 192. The sources232, 234 may be alternated in a manner analogous to those described forthe first arrangement 210.

The fourth arrangement 216 of FIG. 6 is similar to the secondarrangement 212, except that the illumination sources 236, 238 on theright side of the probe head 192 are turned off and on together,alternately with the illumination sources 240, 242 on the left side ofthe probe head 192. Thus, two sets of spectral data may be obtained fora given region, one set using the illumination sources 236, 238 on theright of the midline 244, and the other set using the illuminationsources 240, 242 on the left of the midline 244.

FIG. 7 depicts exemplary illumination of a region 250 of a tissue sample194 using light incident to the region 250 at two different angles 252,254 according to an illustrative embodiment of the invention. FIG. 7demonstrates that source light position may affect whether data isaffected by glare. The probe head 192 of FIG. 7 is depicted in acut-away view for illustrative purposes. In this illustrativeembodiment, the top illumination source 188 and bottom illuminationsource 190 are turned on sequentially and illuminate the surface of atissue sample 194 at equal and opposite angles relative to thecollection axis 256. Arrows represent the light emitted 252 from the topillumination source 188, and the light specularly reflected 258 from thesurface of the region 250 of the tissue sample 194. In preferredembodiments, it is desired to collect diffusely reflected light, asopposed to specularly reflected light 258 (glare). Since the specularlyreflected light 258 from the top illumination source 188 does not enterthe collecting optics 200 in the example illustrated in FIG. 7, a set ofdata obtained using the top illumination source 188 would not beaffected by glare.

However, in the example illustrated in FIG. 7, the emitted light 254from the bottom illumination source 190 reaches the surface of theregion 250 of the tissue 194 and is specularly reflected into thecollecting optics 200, shown by the arrow 260. Data obtained using thebottom illumination source 190 in the example pictured in FIG. 7 wouldbe affected by glare. This data may not be useful, for example, indetermining a characteristic or a condition of the region 250 of thetissue 194. In this example, it would be advantageous to instead use theset of data obtained using the top illumination source 188 since it isnot affected by glare.

The position of the collection optics 200 may affect whether or not datais affected by glare. For example, light 252 with illumination intensityI_(o)(λ) strikes a tissue surface at a given region 250. A fraction ofthe initial illumination intensity, αI_(o)(λ), is specularly reflectedfrom the surface 258, where α is a real number between 0 and 1. Anacceptance cone 268 is the space through which light is diffuselyreflected from the tissue 194 into the collecting optics 200, in thisembodiment. Light may also be emitted or otherwise transmitted from thesurface of the tissue. The diffusely reflected light is of interest,since spectral data obtained from diffusely reflected light can be usedto determine the condition of the region of the sample. If there is nospecular reflection within the acceptance cone 268, only diffuselyreflected light is collected, and the collected signal corresponds toI_(t)(λ), where I_(t)(λ) is the intensity of light diffusely reflectedfrom the region 250 on the surface of the tissue.

If the collection optics 200 are off-center, light incident to thetissue surface may specularly reflect within the acceptance cone 268.For example, light with illumination intensity I_(o)(λ) strikes thesurface of the tissue. Light with a fraction of the initial illuminationintensity, αI_(o)(λ), from a given source is specularly reflected fromthe surface 266, where α is a real number between 0 and 1. Where thereis specular reflection of light within the acceptance cone 268, bothdiffusely reflected light and specularly reflected light reach thecollecting optics 200. Thus, the collected signal corresponds to anintensity represented by the sum I_(t)(λ)+αI_(o)(λ). It may be difficultor impossible to separate the two components of the measured intensity,thus, the data may not be helpful in determining the condition of theregion of the tissue sample due to the glare effect.

FIG. 8 is a diagram 284 depicting illumination of a region 250 of acervical tissue sample 194 using a probe 142 and a vaginal speculum 286according to an illustrative embodiment of the invention. Here, theilluminating light incident to the tissue sample 194, is depicted by theupper and lower intersecting cones 196, 198. In a preferred embodiment,the probe 142 operates without physically contacting the tissue beinganalyzed. In one embodiment, a disposable sheath 146 is used to coverthe probe head 192, for example, in case of incidental contact of theprobe head 192 with the patient's body. FIG. 9 is a schematicrepresentation of an accessory device 290 that forms at least part ofthe disposable sheath 146 for a probe head 192 according to anillustrative embodiment of the invention. In one illustrativeembodiment, the entire sheath 146, including the accessory device 290,if present, is disposed of after a single use on a patient. As shown inFIG. 8, in one illustrative embodiment, the disposable sheath 146 and/orthe accessory device 290 have a unique identifier, such as atwo-dimensional bar code 292. According to an illustrative feature, theaccessory device 290 is configured to provide an optimal light pathbetween the optical probe 142 and the target tissue 194. Optionaloptical elements in the accessory device 290 may be used to enhance thelight transmitting and light receiving functions of the probe 142.

Although an illustrative embodiment of the invention is described hereinwith respect to analysis of vaginal tissue, other tissue types may beanalyzed using these methods, including, for example, colorectal,gastroesophageal, urinary bladder, lung, skin tissue, and/or any tissuecomprising epithelial cells.

Spectral Calibration—110, 112, 116

FIG. 10 is a block diagram 300 featuring components of the tissuecharacterization system 100 of FIG. 1 that involve spectral datacalibration and correction, according to an illustrative embodiment ofthe invention. The instrument 102 of FIG. 1 is calibrated at thefactory, prior to field use, and may also be calibrated at regularintervals via routine preventive maintenance (PM). This is referred toas factory and/or preventive maintenance calibration 110. Additionally,calibration is performed immediately prior to each patient scan toaccount for temporal and/or intra-patient sources of variability. Thisis referred to as pre-patient calibration 116. The illustrativeembodiment includes calibrating one or more elements of the instrument102, such as the spectrometer and detector 168 depicted in FIG. 3.

Calibration includes performing tests to adjust individual instrumentresponse and/or to provide corrections accounting for individualinstrument variability and/or individual test (temporal) variability.During calibration procedures, data is obtained for the pre-processingof raw spectral data from a patient scan. The tissue classificationsystem 100 of FIG. 1 includes determining corrections based on thefactory and/or preventive maintenance calibration tests, indicated byblock 112 in FIG. 10 and in FIG. 1. Where multiple sets of factoryand/or preventive maintenance (PM) data exists, the most recent set ofdata is generally used to determine correction factors and topre-process spectral data from a patient scan. Corrections are alsodetermined based on pre-patient calibration tests, indicated by block118 of FIG. 10. The correction factors are used, at least indirectly, inthe pre-processing (114, FIG. 1) of fluorescence and reflectancespectral data obtained using a UV light source and two white lightsources. Block 114 of FIG. 11 corresponds to the pre-processing ofspectral data in the overall tissue classification system 100 of FIG. 1,and is further discussed herein.

Calibration accounts for sources of individual instrument variabilityand individual test variability in the preprocessing of raw spectraldata from a patient scan. Sources of instrument and individual testvariability include, for example, external light (light originatingoutside the instrument 102, such as room light) and internal straylight. Internal stray light is due at least in part to internal “crosstalk,” or interaction between transmitted light and the collectionoptics 200. Calibration also accounts for the electronic backgroundsignal read by the instrument 102 when no light sources, internal orexternal, are in use. Additionally, calibration accounts for variationsin the amount of light energy delivered to a tissue sample during ascan, spatial inhomogeneities of the illumination source(s), chromaticaberration due to the scanning optics, variation in the wavelengthresponse of the collection optics 200, and/or the efficiency of thecollection optics 200, for example, as well as other effects.

In the illustrative embodiment of FIG. 10, factory and preventivemaintenance calibration tests are performed to determine correctionfactors 112 to apply to raw fluorescence and reflectance spectral dataobtained during patient scans. The factory/preventive maintenancecalibration tests 110 include a wavelength calibration test 302, a“null” target test 304, a fluorescent dye cuvette test 306, a tungstensource test 308, an “open air” target test 310, a customized target test312, and a NIST standard target test 314.

The wavelength calibration test 302 uses mercury and argon spectra toconvert a CCD pixel index to wavelengths (nm). A wavelength calibrationand interpolation method using data from the mercury and argoncalibration test 302 is described below.

The null target test 304 employs a target having about 0% diffusereflectivity and is used along with other test results to account forinternal stray light. Data from the factory/PM null target test 304 areused to determine the three correction factors shown in block 316 forfluorescence spectral measurements (F) obtained using a UV light source,and broadband reflectance measurements (BB1, BB2) obtained using each oftwo white light sources. In one embodiment, these three correctionfactors 316 are used in determining correction factors for other tests,including the factory/PM fluorescent dye cuvette test 306, thefactory/PM open air target test 310, the factory/PM customized targettest 312, and the factory/PM NIST standard target test 314. The open airtarget test 310, the customized target test 312, and the NIST standardtarget test 314 are used along with the null target test 304 to correctfor internal stray light in spectral measurements obtained using a UVlight source and one or more white light sources.

The open air target test 310 is performed without a target and in theabsence of external light (all room lights turned off). The customizedtarget test 312 employs a custom-designed target including a material ofapproximately 10% diffuse reflectivity and is performed in the absenceof external light. The custom-designed target also containsphosphorescent and fluorescent plugs that are used during instrumentfocusing and target focus validation 122. In one embodiment, thecustom-designed target is also used during pre-patient calibrationtesting (116, 330) to monitor the stability of fluorescence readingsbetween preventive maintenance procedures and/or to align an ultraviolet(UV) light source 160—for example, a nitrogen laser or afrequency-tripled Nd:YAG laser. The NIST (U.S. National Institute ofStandards and Technology) standard target test 314 employs aNIST-standard target comprising a material of approximately 60% diffusereflectivity and is performed in the absence of external light.Correction factors determined from the “open air” target test 310, thecustom target test 312, and the NIST-standard target test 314 are shownin blocks 322, 324, and 326 of FIG. 10, respectively. The correctionfactors are discussed in more detail below.

The fluorescent dye cuvette test 306 accounts for the efficiency of thecollection optics 200 of a given unit. The illustrative embodiment usesdata from the fluorescent dye cuvette test 306 to determine a scalarcorrection factor 318 for fluorescence measurements (F) obtained using aUV light source. The tungsten source test 308 uses aquartz-tungsten-halogen lamp to account for the wavelength response ofthe fluorescence collection optics 200, and data from this test are usedto determine a correction factor 320 for fluorescence measurements (F)obtained using a UV light source.

In addition to factory and preventive maintenance calibration 110,pre-patient calibration 116 is performed immediately before each patientscan. The pre-patient calibration 116 includes performing a null targettest 328 and a customized target test 330 before each patient scan.These tests are similar to the factory/PM null target test 304 and thefactory/PM custom target test 312, except that they are each performedunder exam room conditions immediately before a patient scan isconducted. The correction factors shown in blocks 332 and 334 of FIG. 10are determined from the results of the pre-patient calibration tests.Here, correction factors (316, 322) from the factory/PM null target test304 and the factory/PM open air test 310 are used along with pre-patientcalibration data to determine the pre-patient correction factors 118,which are used, in turn, to pre-process raw spectral data from a patientscan, as shown, for example, in FIG. 11.

FIG. 11 is a block diagram 340 featuring the spectral datapre-processing component 114 of the tissue characterization system 100of FIG. 1 according to an illustrative embodiment of the invention. InFIG. 11, “F” represents the fluorescence data obtained using the UVlight source 160, “BB1” represents the broadband reflectance dataobtained using the first 188 of the two white light sources 162 and“BB2” represents the broadband reflectance data obtained using thesecond 190 of the two white light sources 162. Blocks 342 and 344indicate steps undertaken in pre-processing raw reflectance dataobtained from the tissue using each of the two white light sources 188,190, respectively. Block 346 indicates steps undertaken inpre-processing raw fluorescence data obtained from the tissue using theUV light source 160. These steps are discussed in more detail below.

The instrument 102 detailed in FIG. 3 features a scanner assembly 180which includes a CCD (charge couple device) detector and spectrographfor collecting fluorescence and reflectance spectra from tissue samples.Because a CCD detector is used, the system employs a calibrationprocedure to convert a pixel index into wavelength units. Referring toFIG. 10, the pixel-to-wavelength calibration 302 is performed as part offactory and/or preventive maintenance calibration procedures 110.

In the illustrative embodiment, the tissue classification system 100uses spectral data obtained at wavelengths within a range from about 360nm to about 720 nm. Thus, the pixel-to-wavelength calibration procedure302 uses source light that produces peaks near and/or within the 360 nmto 720 nm range. A mercury lamp produces distinct, usable peaks betweenabout 365 nm and about 578 nm, and an argon lamp produces distinct,usable peaks between about 697 nm and about 740 nm. Thus, theillustrative embodiment uses mercury and argon emission spectra toconvert a pixel index from a CCD detector into units of wavelength (nm).

First, a low-pressure pen-lamp style mercury lamp is used as sourcelight, and intensity is plotted as a function of pixel index. The pixelindices of the five largest peaks are correlated to ideal, standard Hgpeak positions in units of nanometers. Second, a pen-lamp style argonlamp is used as source light and intensity is plotted as a function ofpixel index. The two largest peaks are correlated to ideal, standard Arpeak positions in units of nanometers.

The seven total peaks provide a set of representative peakswell-distributed within a range from about 365 nm to about 738nm—comparable to the range from about 360 nm to about 720 nm that isused for data analysis in the tissue classification system 100. Thecalibration procedure in block 302 of FIG. 10 includes retrieving thefollowing spectra: a spectrum using a mercury lamp as light source, amercury background spectrum (a spectrum obtained with the mercury sourcelight turned off), a spectrum using an argon lamp as light source, andan argon background spectrum. The respective Hg and Ar backgroundspectra are subtracted from the Hg and Ar spectra, producing thebackground-corrected Hg and Ar spectra. The spectra are essentiallynoise-free and require no smoothing. Each of the seven pixel valuescorresponding to the seven peaks above are determined by finding thecentroid of the curve of each peak over a +/−5 pixel range of themaximum as shown in Equation 1:

$\begin{matrix}{{{centroid} = \frac{\int_{p_{\max}^{- 5}}^{p_{\max}^{+ 5}}{p\mspace{11mu} I_{p}\mspace{11mu}{\mathbb{d}p}}}{\int_{p_{\max}^{- 5}}^{p_{\max}^{+ 5}}{I_{p}\mspace{11mu}{\mathbb{d}p}}}},} & (1)\end{matrix}$where p is pixel value, I_(p) is the intensity at pixel p, and p_(max)is the pixel value corresponding to each peak maximum. From the p_(max)determinations, a polynomial function correlating pixel value towavelength value is determined by performing a least-squares fit of thepeak data. In one embodiment, the polynomial function is of fourthorder. In alternative embodiments, the polynomial is of first order,second order, third order, fifth order, or higher order.

Alternatively to finding p_(max) by determining the centroid asdiscussed above, in another illustrative embodiment thepixel-to-wavelength calibration procedure 302 includes fitting a secondorder polynomial to the signal intensity versus pixel index data foreach of the seven peaks around the maximum +/−3 pixels (range including7 pixels); taking the derivative of the second order polynomial; andfinding the y-intercept to determine each p_(max).

The resulting polynomial function correlating pixel value to wavelengthvalue is validated, for example, by specifying that the maximum argonpeak be located within a given pixel range, such as [300:340] and/orthat the intensity count at the peak be within a reasonable range, suchas between 3000 and 32,000 counts. Additionally, the maximum mercurypeak is validated to be between pixel 150 and 225 and to produce anintensity count between 3000 and 32,000 counts. Next, the maximumdifference between any peak wavelength predicted by the polynomialfunction and its corresponding ideal (reference) peak is required to bewithin about 1.0 nm. Alternatively, other validation criteria may beset.

Additional validation procedures may be performed to compare calibrationresults obtained for different units, as well as stability ofcalibration results over time. In one illustrative embodiment, thepixel-to-wavelength calibration 302 and/or validation is performed aspart of routine preventive maintenance procedures.

Since fluorescence and reflectance spectral data that are used asreference data in the classification system 100 may be obtained atmultiple clinical sites with different individual instruments, theillustrative system 100 standardizes spectral data in step 302 of FIG.10 by determining and using values of spectral intensity only atdesignated values of wavelength. Spectral intensity values arestandardized by interpolating pixel-based intensities such that theycorrespond to wavelengths that are spaced every 1 nm between about 360nm and about 720 nm. This may be done by linear interpolation of thepixel-based fluorescence and/or reflectance values. Other illustrativeembodiments use, for example, a cubic spline interpolation procedureinstead of linear interpolation.

In some illustrative embodiments, spectral data acquisition duringpatient scans and during the calibration procedures of FIG. 10 includesthe use of a CCD array as part of the scanner assembly 180 depicted inFIG. 3. The CCD array may contain any number of pixels corresponding todata obtained at a given time and at a given interrogation point. In oneembodiment, the CCD array contains about 532 pixels, including unusedleading pixels from index 0 to 9, relevant data from index 10 to 400, apower monitor region from index 401 to 521, and unused trailing pixelsfrom index 522 to 531. One embodiment includes “power correcting” or“power monitor correcting” by scaling raw reflectance and/orfluorescence intensity measurements received from a region of a tissuesample with a measure of the intensity of light transmitted to theregion of the tissue sample. In order to provide the scaling factor, theinstrument 102 directs a portion of a light beam onto the CCD array, forexample, at pixel indices 401 to 521, and integrates intensity readingsover this portion of the array.

In one preferred embodiment, both factory/PM 110 and pre-patient 116calibration accounts for chromatic, spatial, and temporal variabilitycaused by system interference due to external stray light, internalstray light, and electronic background signals. External stray lightoriginates from sources external to the instrument 102, for example,examination room lights and/or a colposcope light. The occurrence andintensity of the effect of external stray light on spectral data isvariable and depends on patient parameters and the operator's use of theinstrument 102. For example, as shown in FIG. 8, the farther the probehead 192 rests from the speculum 286 in the examination of cervicaltissue, the greater the opportunity for room light to be present on thecervix. The configuration and location of a disposable component 146 onthe probe head 192 also affects external stray light that reaches atissue sample. Additionally, if the operator forgets to turn off thecolposcope light before taking a spectral scan, there is a chance thatlight will be incident on the cervix and affect spectral data obtained.

Electronic background signals are signals read from the CCD array whenno light sources, internal or external, are in use. According to theillustrative embodiment, for all components of the tissuecharacterization system 100 that involve obtaining and/or using spectraldata, including components 110, 116, 104, and 114 of FIG. 1, bothexternal stray light and electronic background signals are taken intoaccount by means of a background reading. For each interrogation pointin a spectral scan in which one or more internal light sources are used,a background reading is obtained in which all internal light sources(for example, the Xenon lamps and the UV laser) are turned off.According to one feature, the background reading immediately precedesthe fluorescence and broadband reflectance measurements at each scanlocation, and the system 100 corrects for external stray light andelectronic background by subtracting the background reading from thecorresponding spectral reading at a given interrogation point. In FIG.10, each calibration test—including 304, 306, 308, 310, 312, 314, 328,and 330—includes obtaining a background reading at each interrogationpoint and subtracting it from the test reading to account for externalstray light and electronic background signals. Also, backgroundsubtraction is a step in the spectral data preprocessing 114 methods inFIG. 11, for the pre-processing of raw BB1 and BB2 reflectance data 342,344 as well as the pre-processing of raw fluorescence data 346.

Equation 2 shows the background correction for a generic spectralmeasurement from a tissue sample, S_(tissue+ISL+ESL+EB)(i,λ):S _(tissue+ISL)(i,λ)=S _(tissue+ISL+ESL+EB)(i,λ)−Bk _(EB+ESL)(i,λ)  (2)where i corresponds to a scan location; λ is wavelength or its pixelindex equivalent; and subscripts denote influences on the spectralmeasurement—where “tissue” represents the tissue sample, “ISL”represents internal stray light (internal to the instrument 102), “ESL”represents external stray light, and “EB” represents electronicbackground. S_(tissue+ISL+ESL+EB)(i,λ) is a two-dimensional array (whichmay be power-monitor corrected) of spectral data obtained from thetissue at each interrogation point (region) i as a function ofwavelength λ; and Bk_(EB+ESL)(i,λ) is a two-dimensional arrayrepresenting values of the corresponding background spectral readings ateach point i as a function of wavelength λ. S_(tissue+ISL)(i,λ) is thebackground-subtracted spectral array that is thereby corrected foreffects of electronic background (EB) and external stray light (ESL) onthe spectral data from the tissue sample. The electronic backgroundreading is subtracted on a wavelength-by-wavelength,location-by-location basis. Subtracting the background reading generallydoes not correct for internal stray light (ISL), as denoted in thesubscript of S_(tissue+ISL)(i,λ).

Internal stray light includes internal cross talk and interactionbetween the transmitted light within the system and the collectionoptics. For fluorescence measurements, a primary source of internalstray light is low-level fluorescence of optics internal to the probe142 and the disposable component 146. For reflectance measurements, aprimary source of internal stray light is light reflected off of thedisposable 146 and surfaces in the probe 142 that is collected throughthe collection optics 200. The positioning of the disposable 146 cancontribute to the effect of internal stray light on reflectancemeasurements. For example, the internal stray light effect may vary overinterrogation points of a tissue sample scan in a non-random,identifiable pattern due to the position of the disposable during thetest.

According to the illustrative embodiment of FIG. 10, the factory/PM nulltarget test 304, the factory/PM open air target test 306, the factory/PMcustom target test 312, the factory/PM NIST target test 314, thepre-patient null target test 328, and the pre-patient custom target test330 provide correction factors to account for internal stray lighteffects on fluorescence and reflectance spectral measurements. In analternative illustrative embodiment, a subset of these tests is used toaccount for internal stray light effects.

The null target test 304, 328, performed in factory/preventivemaintenance 110, and pre-patient 116 calibration procedures, uses atarget that has a theoretical diffuse reflectance of 0%, although theactual value may be higher. Since, at least theoretically, no light isreflected by the target, the contribution of internal stray light can bemeasured for a given internal light source by obtaining a spectrum froma region or series of regions of the null target with the internal lightsource turned on, obtaining a background spectrum from the null targetwith the internal light source turned off, and background-subtracting toremove any effect of electronic background signal or external straylight. The background-subtracted reading is then a measure of internalstray light. The pre-patient null target test 328 takes into accountspatially-dependent internal stray light artifacts induced by theposition of a disposable 146, as well as temporal variability induced,for example, by the aging of the instrument and/or dust accumulation. Inone embodiment, the factory/PM null target test 304 is used incalculating correction factors from other factory and/or preventivemaintenance calibration procedures. The null target tests 304, 328 arenot perfect, and improved measurements of the effect of internal straylight on spectral data can be achieved by performing additional tests.

The open air target test 310 is part of the factory preventivemaintenance (PM) calibration procedure 110 of FIG. 10 and provides acomplement to the null target tests 304, 328. The open air target test310 obtains data in the absence of a target with the internal lightsources turned on and all light sources external to the device turnedoff, for example, in a darkroom. The null target test 304, by contrast,does not have to be performed in a darkroom since it uses a target inplace in the calibration port, thereby sealing the instrument such thatmeasurements of light from the target are not affected by externallight. Although a disposable 146 is in place during open air testmeasurements, the factory/PM open air target test 310 does not accountfor any differences due to different disposables used in each patientrun. The open air measurements are important in some embodiments,however, since they are performed under more controlled conditions thanpre-patient calibration tests 116, for example, the open air tests maybe performed in a darkroom. Also, the factory/PM calibration 110measurements account for differences between individual instruments 102,as well as the effects of machine aging—both important factors sincereference data obtained by any number of individual instruments 102 arestandardized for use in a tissue classification algorithm, such as theone depicted in block 132 of FIG. 1.

FIGS. 12, 13, 14, and 15 show graphs demonstrating meanbackground-subtracted, power-monitor-corrected intensity readings from afactory open air target test 310 and a null target test 304 using a BB1reflectance white light source and a UV light source (laser). FIG. 12shows a graph 364 of mean intensity 366 from an open air target testover a set of regions as a function of wavelength 368 using a BB1reflectance white light source 188—the “top” source 188 as depicted inFIGS. 4, 7, and 8. FIG. 13 shows a graph 372 of mean intensity 366 froma null target test over the set of regions as a function of wavelength368 using the same BB1 light source. Curves 370 and 374 are comparablebut there are some differences.

FIG. 14 shows a graph 376 of mean intensity 378 from an open air targettest over a set of regions as a function of wavelength 380 using a UVlight source, while FIG. 15 shows a graph 384 of mean intensity 378 froma null target test over the set of regions as a function of wavelength380 using the UV light source. Again, curves 382 and 386 are comparable,but there are some differences between them. Differences between theopen air test intensity and null target test intensity are generallyless than 0.1% for reflectance data and under 1 count/μJ forfluorescence data.

Accounting for internal stray light is more complicated for reflectancemeasurements than for fluorescence measurements due to an increasedspatial dependence. The open air target test measurement, in particular,has a spatial profile that is dependent on the position of thedisposable.

FIG. 16 shows a representation 390 of regions of an exemplary scanperformed in a factory open air target test. The representation 390,shows that broadband intensity readings can vary in a non-random,spatially-dependent manner. Other exemplary scans performed in factoryopen air target tests show a more randomized, less spatially-dependentvariation of intensity readings than the scan shown in FIG. 16.

According to the illustrative embodiment, the system 100 of FIG. 1accounts for internal stray light by using a combination of the resultsof one or more open air target tests 310 with one or more null targettests 304, 328. In an alternative embodiment, open air target test datais not used at all to correct for internal stray light, pre-patient nulltarget test data being used instead.

Where open air and null target test results are combined, it is helpfulto avoid compounding noise effects from the tests. FIG. 17 shows a graph402 depicting as a function of wavelength 406 the ratio 404 of thebackground-corrected, power-monitor-corrected reflectance spectralintensity at a given region using an open air target to the reflectancespectral intensity at the region using a null target according to anillustrative embodiment of the invention. The raw data 407 is shown inFIG. 17 fit with a second-order polynomial 412, and fit with athird-order polynomial without filtering 410, and with filtering 408. Asseen by the differences between curve 407 and curves 408, 410, and 412,where a ratio of open air target data and null target data are used tocorrect for internal stray light in reflectance measurements, a curvefit of the raw data reduces the effect of noise. This is shown in moredetail herein with respect to the calculation of pre-patient corrections118 in FIG. 10. Also evident in FIG. 17 is that the open air measurementgenerally differs from the null target measurement, since the ratio 404is not equal to 1, and since the ratio 404 has a distinct wavelengthdependence.

FIG. 18 shows a graph 414 depicting as a function of wavelength 418 theratio 416 of fluorescence spectral intensity using an open air target tothe fluorescence spectral intensity using a null target according to anillustrative embodiment of the invention. The raw data 420 does notdisplay a clear wavelength dependence, except that noise increases athigher wavelengths. A mean 422 based on the ratio data 420 over a rangeof wavelengths is plotted in FIG. 18. Where a ratio of open air targetto null target data is used to correct for internal stray light influorescence measurements, using a mean value calculated from raw dataover a stable range of wavelength reduces noise and does not ignore anyclear wavelength dependence.

FIG. 10 shows correction factors corresponding to open air 310 and nulltarget 304, 328 calibration tests in one embodiment that compensatesspectral measurements for internal stray light effects. There are threetypes of spectral measurements in FIG. 10—fluorescence (F) measurementsand two reflectance measurements (BB1, BB2) corresponding to dataobtained using a UV light source and two different white light sources,respectively. The corrections in blocks 316, 322, and 332 come from theresults of the factory/PM null target test 304, the factory/PM open airtarget test 310, and the pre-patient null target test 328, respectively,and these correction factors are applied in spectral data pre-processing(FIG. 11) to compensate for the effects of internal stray light. Thesecorrection factors are described below in terms of this embodiment.

Block 316 in FIG. 10 contains correction factors computed from theresults of the null target test 304, performed during factory and/orpreventive maintenance (PM) calibration. The null target test includesobtaining a one-dimensional array of mean values of spectral data fromeach channel—F, BB1, and BB2—corresponding to the three different lightsources, as shown in Equations 3, 4, and 5:FCNULLFL=<I _(nt,F)(i,λ,t _(o))>_(i)  (3)FCNULLBB1=<I _(nt,BB1)(i,λ,t _(o))>_(i)  (4)FCNULLBB2=<I _(nt,BB2)(i,λ,t _(o))>_(i)  (5)where I_(nt) refers to a background-subtracted, power-monitor-correctedtwo-dimensional array of spectral intensity values; subscript F refersto intensity data obtained using the fluorescence UV light source;subscripts BB1 and BB2 refer to intensity data obtained using thereflectance BB1 and BB2 white light sources, respectively; i refers tointerrogation point “i” on the calibration target; λ refers to awavelength at which an intensity measurement corresponds or itsapproximate pixel index equivalent; t_(o) refers to the fact themeasurement is obtained from a factory or preventive maintenance test,the “time” the measurement is made; and < >_(i) represents aone-dimensional array (spectrum) of mean values computed on apixel-by-pixel basis for each interrogation point, i. In thisembodiment, a one-dimensional array (spectrum) of fluorescence valuescorresponding to wavelengths from λ=370 nm to λ=720 nm is obtained ateach of 499 interrogation points, i. An exemplary scan pattern 202 of499 interrogation points appears in FIG. 5. In the illustrativeembodiment, data from an additional interrogation point is obtained froma region outside the target 206. Each of the reflectance intensityspectra is obtained over the same wavelength range as the fluorescenceintensity spectra, but the BB1 data is obtained at each of 250interrogation points over the bottom half of the target and the BB2 datais obtained at each of 249 interrogation points over the top half of thetarget. This avoids a shadowing effect due to the angle at which thelight from each source strikes the target during the null target test304. Values of the most recent factory or preventive maintenancecalibration test, including the factory/PM null target test 304, areused in spectral data pre-processing (FIG. 11) for each patient scan.

The pre-patient null target test, shown in block 328 of FIG. 10, issimilar to the factory/PM null target test 304, except that it isperformed just prior to each patient test scan. Each pre-patient nulltarget test 328 produces three arrays of spectral data as shown below:I_(nt,F)(i,λ,t′)  (6)I_(nt,BB1)(i,λ,t′)  (7)I_(nt,BB2)(i,λ,t′)  (8)where t′ refers to the fact the measurements are obtained just prior tothe test patient scan, as opposed to during factory/PM testing (t_(o)).

Block 332 in FIG. 10 contains correction factors from the open airtarget test 310, preformed during factory and/or preventive maintenance(PM) calibration 110. The open air target test is performed with thedisposable in place, in the absence of a target, with the internal lightsources turned on, and with all light sources external to the deviceturned off. The open air target test 310 includes obtaining an array ofspectral data values from each of the three channels—F, BB1, and BB2—asshown below:I_(oa,F)(i,λ,t_(o))  (9)I_(oa,BB1)(i,λ,t_(o))  (10)I_(oa,BB2)(i, λ,t_(o))  (11)

In each of items 9, 10, and 11 above, I_(oa) refers to abackground-subtracted, power-monitor-corrected array of spectralintensity values; i runs from interrogation points 1 to 499; and λ runsfrom 370 nm to 720 nm (or the approximate pixel index equivalent).

According to the illustrative embodiment, correction for internal straylight makes use of both null target test results and open air targettest results. Correction factors in block 322 of FIG. 10 use resultsfrom the factory/PM null target test 304 and factory/PM open air targettest 310. The correction factors in block 322 are computed as follows:sFCOFL=[<I _(oa,F)(i,λ,t _(o))>_(i) /<I _(nt,F)(i,λ,t_(o))>_(i)]_(mean, λ=)375 nm to 470 nm  (12)FCOBB1=fitted form of <I _(oa,BB1)(i,λ,t _(o))>_(i) /<I _(nt,BB1)(i,λ,t_(o))>_(i)  (13)FCOBB2=fitted form of <I _(oa,BB2)(i,λ,t _(o))>_(i) /<I _(nt,BB2)(i,λ,t_(o))>_(i)  (14)where < >_(i) represents a spectrum (1-dimensional array) of mean valuescomputed on a pixel-by-pixel basis for each interrogation point i, andwhere < >_(i)/< >_(i) represents a spectrum (1-dimensional array) ofquotients (ratios of means) computed on a pixel-by-pixel basis for eachinterrogation point i. The correction factor sFCOFL in Equation 12 is ascalar quantity representing the mean value of the 1-dimensional arrayin brackets [ ] across pixel indices corresponding to the wavelengthrange of about 375 nm to about 470 mm.

FIG. 18 shows an example value of sFCOFL 422 evaluated using a set ofmean open air spectral data and mean null target spectral data. Largeoscillations are damped by using the mean in Equation 12. Otherwavelength ranges can be chosen instead of the wavelength range of about375 nm to about 470 nm.

The one-dimensional arrays, FCOBB1 and FCOBB2, are obtained bycurve-fitting the spectra of quotients in Equations 13 and 14 withsecond-order polynomials and determining values of the curve fitcorresponding to each pixel. FIG. 17 shows an example curve fit forFCOBB1 (412). Unlike the fluorescence measurements, there is wavelengthdependence of this ratio, and a curve fit is used to properly reflectthis wavelength dependence without introducing excessive noise infollowing computations.

Block 332 in FIG. 10 contains correction factors using results from thepre-patient null target test 328, as well as the most recent factory/PMnull target test 304 and open air target test 310. The correctionfactors in block 332 are computed as follows:SLFL=sFCOFL·<I _(nt,F)(i,λ,t′)>_(i)  (15)SLBB1=FCOBB1·<I _(nt,BB1)(i,λ,t′)>_(i)  (16)SLBB2=FCOBB2·<I _(nt,BB2)(i,λ,t′)> _(i)  (17)where Equation 15 represents multiplying each value in the fluorescencemean pre-patient null target spectrum by the scalar quantity sFCOFL fromEquation 12; Equation 16 represents multiplying corresponding elementsof the mean pre-patient null target BB1 spectrum and the one-dimensionalarray FCOBB1 from Equation 13; and Equation 17 represents multiplyingcorresponding elements of the mean pre-patient null target BB2 spectrumand the one-dimensional array FCOBB2 from Equation 14. Each of SLFL,SLBB1, and SLBB2 is a one-dimensional array.

The correction factors in block 332 of FIG. 10 represent thecontribution due to internal stray light (ISL) for a given set ofspectral data obtained from a given patient scan. Combining equationsabove:SLFL=[<I _(oa,F)(i,λ,t _(o))>_(i) /<I _(nt,F)(i,λ,t_(o))>_(i)]_(mean, λ=375 nm to 470) nm·(I _(nt,F)(i,λ,t′)>_(i)  (18)SLBB1=[<I _(oa,BB1)(i,λ,t _(o))>_(i) /<I _(nt,BB1)(i,λ,t_(o))>_(i)]_(fitted) ·<I _(nt,BB1)(i,λ,t _(o))>_(i)  (19)SLBB2=[<I _(oa,BB2)(i,λ,t _(o))>_(i) /<I _(nt,BB2)(i,λ,t_(o))>_(i)]_(fitted) ·<I _(nt,BB2)(i,λ,t′)>_(i)  (20)

Alternative internal stray light correction factors are possible. Forexample, in one alternative embodiment, the scalar quantity in Equation18 is replaced with the value 1.0. In one alternative embodiment, thefirst term on the right side of either or both of Equation 19 andEquation 20 is replaced with a scalar quantity, for example, a meanvalue or the value 1.0.

Spectral data preprocessing 114 as detailed in FIG. 11 includescompensating for internal stray light effects as measured by SLFL, SLBB1and SLBB2. In one embodiment, a patient scan includes the acquisition ateach interrogation point in a scan pattern (for example, the 499-pointscan pattern 202 shown in FIG. 5) of a set of raw fluorescence intensitydata using the UV light source 160, a first set of raw broadbandreflectance intensity data using a first white light source (162, 188),a second set of raw broadband reflectance intensity data using a secondwhite light source (162, 192), and a set of raw background intensitydata using no internal light source, where each set of raw data spans aCCD pixel index corresponding to a wavelength range between about 370 nmand 720 nm. In another embodiment, the wavelength range is from about370 nm to about 700 nm. In another embodiment, the wavelength range isfrom about 300 nm to about 900 nm. Other embodiments include the use ofdifferent wavelength ranges.

The raw background intensity data set is represented as thetwo-dimensional array Bkgnd[ ] in FIG. 11. Spectral data processing 114includes subtracting the background array, Bkgnd[ ], from each of theraw BB1, BB2, and F arrays on a pixel-by-pixel and location-by-locationbasis. This accounts at least for electronic background and externalstray light effects, and is shown as item #1 in each of blocks 342, 344,and 346 in FIG. 11.

Also, each CCD array containing spectral data includes a portion formonitoring the power output by the light source used to obtain thespectral data. In one embodiment, the intensity values in this portionof each array are added or integrated to provide a one-dimensional arrayof scalar values, sPowerMonitor[ ], shown in FIG. 11. Spectral datapre-processing 114 further includes dividing each element of thebackground-subtracted arrays at a given interrogation point by the powermonitor scalar correction factor in sPowerMonitor[ ] corresponding tothe given interrogation point. This allows the expression of spectraldata at a given wavelength as a ratio of received light intensity totransmitted light intensity.

Spectral data pre-processing 114 further includes subtracting each ofthe stray light background arrays—SLBB1, SLBB2, and SLFL—from itscorresponding background-corrected, power-monitor-corrected spectraldata array—BB1, BB2, and F—on a pixel-by-pixel, location-by-locationbasis. This accounts for chromatic, temporal, and spatial variabilityeffects of internal stray light on the spectral data.

The remaining steps in blocks 342 and 344 of the spectral datapre-processing block diagram 340 of FIG. 11 include further factory,preventive maintenance (PM) and/or pre-patient calibration ofreflectance (BB1, BB2) measurements using one or more targets of known,non-zero diffuse reflectance. In the embodiment shown in FIG. 10, thiscalibration uses results from the factory/PM custom target test 312, thefactory/PM NIST-standard target test 314, and the pre-patient customtarget test 330. These calibration tests provide correction factors asshown in blocks 324, 326, and 334 of FIG. 10, that account forchromatic, temporal, and spatial sources of variation in broadbandreflectance spectral measurements. These sources of variation includetemporal fluctuations in the illumination source, spatialinhomogeneities in the illumination source, and chromatic aberration dueto the scanning optics. The broadband reflectance calibration tests(312, 314, 330) also account for system artifacts attributable to bothtransmitted and received light, since these artifacts exist in both testreflectance measurements and known reference measurements.

According to the illustrative embodiment, reflectance, R, computed froma set of regions of a test sample (a test scan) is expressed as inEquation 21:R=[Measurement/Reference Target]·Reflectivity of Reference Target  (21)where R, Measurement, and Reference Target refer to two-dimensional(wavelength, position) arrays of background-corrected, power-correctedand/or internal-stray-light-corrected reflectance data; Measurementcontains data obtained from the test sample; Reference Target containsdata obtained from the reference target; Reflectivity of ReferenceTarget is a known scalar value; and division of the arrays is performedin a pixel-by-pixel, location-by-location manner.

The factory/PM NIST target test 314 uses a 60%, NIST-traceable,spectrally flat diffuse reflectance target in the focal plane, alignedin the instrument 102 represented in FIG. 3. The NIST target test 314includes performing four scans, each of which proceed with the target atdifferent rotational orientations, perpendicular to the optical axis ofthe system. For example, the target is rotated 90° from one scan to thenext. The results of the four scans are averaged on alocation-by-location, pixel-by-pixel basis to remove spatially-dependenttarget artifacts (speckling) and to reduce system noise. The goal is tocreate a spectrally clean (low noise) and spatially-flat data set forapplication to patient scan data. In one embodiment, the NIST targettest 314 is performed only once, prior to instrument 102 use in thefield (factory test), and thus, ideally, is temporally invariant.

The custom target tests 312, 330 use a custom-made target for bothfactory and/or preventive maintenance calibration, as well aspre-patient calibration of reflectance data. The custom target is a 10%diffuse reflective target with phosphorescent and/or fluorescentportions used, for example, to align the ultraviolet (UV) light sourceand/or to monitor the stability of fluorescence readings betweenpreventive maintenance procedures. FIG. 19 is a photograph of the customtarget 426 according to an illustrative embodiment. In FIG. 19, thetarget 426 includes a portion 428 that is about 10% diffuse reflectivematerial, with four phosphorescent plugs 430, 432, 434, 436equally-spaced at the periphery and a single fluorescent plug 438 at thecenter. As a result of the plugs, not all scan locations in the scanpattern 202 of FIG. 5, as applied to the custom target test 426,accurately measure the 10% reflective portion. Thus, a mask provides ameans of filtering out the plug-influenced portions of the custom target426 during a custom target calibration scan 312, 330.

FIG. 20 is a representation of such a mask 444 for the custom targetreflectance calibration tests 312, 330. Area 445 in FIG. 20 correspondsto regions of the custom target 426 of FIG. 19 that are not affected bythe plugs 430, 432, 434, 436, and which, therefore, are usable in thecustom target reflectance calibration tests 312, 330. Areas 446, 448,450, 452, and 454 of FIG. 20 correspond to regions of the custom target426 that are affected by the plugs, and which are masked out in thecustom target calibration scan results.

In the illustrative embodiment, the factory/PM NIST target test 314provides reflectance calibration data for a measured signal from a testsample (patient scan), and the test sample signal is processed accordingto Equation 22:R(i,λ,t′)=[I _(m)(i,λ,t′)/I _(fc)(i,λ,t _(o))]·0.6  (22)

Where R, I_(m), and I_(fc) are two-dimensional arrays ofbackground-corrected, power-corrected reflectance data; R containsreflectance intensity data from the test sample adjusted according tothe reflectance calibration data; I_(m) contains reflectance intensitydata from the sample, I_(fc) contains reflectance intensity data fromthe factory/PM NIST-standard target test 314, and 0.6 is the knownreflectivity of the NIST-standard target. Equation 22 presumes thespectral response of the illumination source is temporally invariantsuch that the factory calibration data from a given unit does not changewith time, as shown in Equation 23 below:I _(fc)(t′)=I _(fc)(t _(o))  (23)However, the spectral lamp function of a xenon flash lamp, as used inthe illustrative embodiment as the white light source 162 in theinstrument 102 of FIG. 3, is not invariant over time.

The illustrative reflectance data spectral preprocessing 114 accountsfor temporal variance by obtaining pre-patient custom target test (330)reflectance calibration data and using the data to adjust data from atest sample, I_(m), to produce adjusted reflectance R, as follows:R(i,λ,t′)=[I _(m)(i,λ,t′)/<I _(cp)(i,λ,t′)>_(i)]·0.1  (24)where masked, mean reflectance intensity data from the pre-patientcustom target test 330 with 10% diffuse reflectivity,<I_(cp)(i,λ,t′)>_(i), replaces I_(fc)(i,λ,t′) in Equation 22. Since thepre-patient custom target test data is updated before every patientexam, the temporal variance effect is diminished or eliminated. In otherillustrative embodiments, various other reference targets may be used inplace of the custom target 426 shown in FIG. 19.

The system 100 also accounts for spatial variability in the targetreference tests of FIG. 10 in pre-processing reflectance spectral data.Illustratively, spatial variability in reflectance calibration targetintensity is dependent on wavelength, suggesting chromatic aberrationsdue to wavelength-dependence of transmission and/or collection opticefficiency.

The illustrative reflectance data spectral preprocessing 114 accountsfor these chromatic and spatial variability effects by obtainingreflectance calibration data and using the data to adjust data from atest sample, I_(m), to produce adjusted reflectance R, as follows:R(i,λ,t′)=[I _(m)(i,λ,t′)/<I _(cp)(i,λ,t′)>_(i) ]·[<I _(fc)(i,λ,t_(o))>_(i) /I _(fc)(i,λ,t _(o))]·0.1  (25)Equation 25 accounts for variations of the intensity response of thelamp by applying the pre-patient custom-target measurements—which areless dependent on differences caused by the disposable—in correctingpatient test sample measurements. Equation 25 also accounts for thespatial response of the illumination source by applying the factoryNIST-target measurements in correcting patient test sample measurements.

In an alternative illustrative embodiment, the NIST-target test 314 isperformed as part of pre-patient calibration 116 to produce calibrationdata, I_(fc)(i,λ,t′), and Equation 22 is used in processing testreflectance data, where the quantity I_(fc)(i,λ,t′) replaces thequantity I_(fc)(i,λ,t_(o)) in Equation 22. According to thisillustrative embodiment, the test data pre-processing procedure 114includes both factory/PM calibration 110 results and pre-patientcalibration 116 results in order to maintain a more consistent basis forthe accumulation and use of reference data from various individual unitsobtained at various times from various patients in a tissuecharacterization system. Thus, this illustrative embodiment usesEquation 26 below to adjust data from a test sample, I_(m), to produceadjusted reflectance R, as follows:R(i,λ,t′)=[I _(m)(i,λ,t′)/<I _(fc)(i,λ,t′)>_(i) ]·[<I _(fc)(i,λ,t_(o))>_(i) /I _(fc)(i,λ,t _(o))]·0.6  (26)where the NIST-standard target test 314 is performed both as afactory/PM test 110 (t_(o)) and as a pre-patient test 116 (t′).

According to the illustrative embodiment, it is preferable to combinecalibration standards with more than one target, each having a differentdiffuse reflectance, since calibration is not then tied to a singlereference value. Here, processing using Equation 25 is preferable toEquation 26. Also, processing via Equation 25 may allow for an easierpre-patient procedure, since the custom target combines functions forboth fluorescence and reflectance system set-up, avoiding the need foran additional target test procedure.

Values of the custom target reflectance in a given individual instrument102 vary over time and as a function of wavelength. For example, FIG. 21shows a graph 458 depicting as a function of wavelength 462 a measure ofthe mean reflectivity 460, R_(cp), of the 10% diffuse target 426 of FIG.19 over the non-masked regions 445 shown in FIG. 20, obtained using thesame instrument on two different days. R_(cp) is calculated as shown inEquation 27:R _(cp)(λ)=[<I _(cp)(i,λ,t _(o))>_(i) /<I _(fc)(i,λ,t _(o))>_(i) ]·R_(fc)  (27)where R_(fc)=0.6, the diffuse reflectance of the NIST-traceable standardtarget. Values of R_(cp) vary as a function of wavelength 462, as seenin each of curves 464 and 466 of FIG. 21. Also, there is a shift fromcurve 464 to curve 466, each obtained on a different day. Similarly,values of R_(cp) vary among different instrument units. Curves 464 and466 show that R_(cp) varies with wavelength and varies from 0.1; thus,assuming R_(cp)=0.1 as in Equation 25 may introduce inaccuracy.

Equation 25 can be modified to account for this temporal and wavelengthdependence, as shown in Equation 28:R(i,λ,t′)=[I _(m)(i,λ,t′)/<I _(cp)(i,λ,t′)>_(i) ]·[<I _(fc)(i, λ,t_(o))>_(i) /I _(fc)(i,λ,t _(o))]·R _(cp,fitted)  (28)where R_(cp,fitted) is an array of values of a second-order polynomialcurve fit of R_(cp) shown in Equation 27. The polynomial curve fitreduces the noise in the R_(cp) array. Other curve fits may be usedalternatively. For example, FIG. 22A shows a graph 490 depicting, forseven individual instruments, curves 496, 498, 500, 502, 504, 506, 508of sample reflectance intensity using the BB1 white light source 188 asdepicted in FIGS. 4, 7 and 8 graphed as functions of wavelength 494.Each of the seven curves represents a mean of reflectance intensity ateach wavelength, calculated using Equation 25 for regions confirmed asmetaplasia by impression. FIG. 22B shows a graph 509 depictingcorresponding curves 510, 512, 514, 516, 518, 520, 522 of test samplereflectance intensity calculated using Equation 28, where R_(cp) varieswith time and wavelength. The variability between individual instrumentunits decreases when using measured values for R_(cp) as in Equation 28rather than as a constant value. The variability between reflectancespectra obtained from samples having a commontissue-class/state-of-health classification, but using differentinstrument units decreases when using measured values for R_(cp) as inEquation 28 rather than a constant value as in Equation 25.

In an alternative embodiment, processing of reflectance data includesapplying Equation 28 without first fitting R_(cp) values to a quadraticpolynomial. Thus, processing is performed in accordance with Equation 29to adjust data from a test sample, I_(m), to produce adjustedreflectance R, as follows:R(i,λ,t′)=[I _(m)(i,λ,t′)/<I _(cp)(i,λ,t′)>_(i) ]·[<I _(fc)(i,λ,t_(o))>_(i) /I _(fc)(i,λ,t _(o))]·R _(cp)  (29)

Applying Equation 29, however, introduces an inconsistency in thereflectance spectra at about 490 nm, caused, for example, by theintensity from the 60% reflectivity factory calibration target exceedingthe linear range of the CCD array. This can be avoided by using a darkerfactory calibration target in the factory NIST target test 314, forexample, a target having a known diffuse reflectance from about 10% toabout 30%.

Results from the factory/PM custom target test 312, the factory/PM NISTtarget test 314, and the pre-patient custom target test 330 provide thecorrection factors shown in blocks 324, 326, and 334, respectively usedin preprocessing reflectance data from a patient scan using the BB1white light source 188 and the BB2 white light source 190 shown in FIGS.4, 7, and 8. Correction factors in block 324 representbackground-subtracted, power-monitor-corrected (power-corrected), andnull-target-subtracted reflectance data from a given factory/PM customtarget test 312 (cp) and are shown in Equations 30 and 31:FCCTMMBB1=<I _(cp,BB1)(i,λ,t _(o))>_(i, masked) −FCNULLBB1  (30)FCCTMMBB2=<I _(cp,BB2)(i,λ,t _(o))>_(i, masked) −FCNULLBB2  (31)where FCNULLBB1 and FCNULLBB2 are given by Equations 4 and 5, and <>_(i), masked represents a one-dimensional array of mean data computedon a pixel-by-pixel basis in regions of area 445 of the scan pattern 444of FIG. 20.

Correction factors in block 326 of FIG. 10 represent ratios ofbackground-subtracted, power-corrected, and null-target-subtractedreflectance data from a factory/PM custom target test 312 (cp) and afactory/PM NIST standard target test 314 (fc) and are shown in Equations32, 33, and 34:

$\begin{matrix}{{{FCBREF1}{\lbrack\rbrack}} = \frac{\left\langle {{I_{{fc},{BB1}}\left( {i,\lambda,t_{o}} \right)}_{{avg}\mspace{14mu}{of}\mspace{14mu} 4} - {FCNULLBB1}} \right\rangle_{i,}}{{I_{{fc},{BB1}}\left( {i,\lambda,t_{o}} \right)}_{{avg}\mspace{14mu}{of}\mspace{14mu} 4} - {FCNULLBB1}}} & (32) \\{{{FCBREF2}{\lbrack\rbrack}} = \frac{\left\langle {{I_{{fc},{BB2}}\left( {i,\lambda,t_{o}} \right)}_{{avg}\mspace{14mu}{of}\mspace{14mu} 4} - {FCNULLBB2}} \right\rangle_{i,}}{{I_{{fc},{BB2}}\left( {i,\lambda,t_{o}} \right)}_{{avg}\mspace{14mu}{of}\mspace{14mu} 4} - {FCNULLBB2}}} & (33) \\{{CALREF} = \left\lbrack {{0.5 \cdot \left( {{FCCTMBB1}/\left\langle {{FCBREF1}{\lbrack\rbrack}} \right\rangle_{i,}} \right)} +} \right.} & (34) \\\left. \mspace{130mu}\left( {{FCCTMBB2}/\left\langle {{FCBREF2}{\lbrack\rbrack}} \right\rangle_{i,}} \right) \right\rbrack_{{interp},{fit}} & \;\end{matrix}$where values of the two-dimensional arrays I_(fc,BB1) and I_(fc,BB2) areaverages of data using the target at each of four positions, rotated 90°between each position; and all divisions, subtractions, andmultiplications are on a location-by-location, pixel-by-pixel basis. Thecorrection factor, CALREF, is a one-dimensional array of values of thequantity in brackets [ ] on the right side of Equation 34, interpolatedsuch that they correspond to wavelengths at 1-nm intervals between λ=360nm and λ720 nm. The interpolated values are then fit with a quadratic orother polynomial to reduce noise.

Correction factors in block 334 of FIG. 10 representbackground-subtracted, power-corrected, internal-stray-light-correctedreflectance data from a pre-patient custom target test 330 (cp) and aregiven in Equations 35 and 36 as follows:BREFMBB1=<I _(cp,BB1)(i,λ,t′)−SLBB1>_(i)  (35)BREFMBB2=<I _(cp,BB2)(i,λ,t′)−SLBB2>_(i)  (36)where SLBB1 and SLBB2 are as shown in Equations 19 and 20.

Steps #4, 5, and 6 in each of blocks 342 and 344 of the spectral datapre-processing block diagram 340 of FIG. 11 include processing patientreflectance data using the correction factors from blocks 324, 326, and334 of FIG. 10 computed using results of the factory/PM custom targettest 312, the factory/PM NIST standard target test 314, and thepre-patient custom target test 330.

In step #4 of block 342 in FIG. 11, the array of background-subtracted,power-corrected, internal-stray-light-subtracted patient reflectancedata obtained using the BB1 light source is multiplied by thetwo-dimensional array correction factor, FCBREF1[ ], and then in step#5, is divided by the correction factor BREFMBB1. After filtering using,for example, a 5-point median filter and a second-order 27-pointSavitsky-Golay filter, the resulting array is linearly interpolatedusing results of the wavelength calibration step 302 in FIG. 10 toproduce a two-dimensional array of spectral data corresponding towavelengths ranging from 360 nm to 720 nm in 1-nm increments at each of499 interrogation points of the scan pattern 202 shown in FIG. 5. Thisarray is multiplied by CALREF in step #6 of block 342 in FIG. 11, andpre-processing of the BB1 spectral data in this embodiment is complete.

Steps #4, 5, and 6 in block 344 of FIG. 11 concern processing of BB2data and is directly analogous to the processing of BB1 data discussedabove.

Steps #4 and 5 in block 346 of FIG. 1 include processing fluorescencedata using factory/PM-level correction factors, applied after backgroundcorrection (step #1), power monitor correction (step #2), and straylight correction (step #3) of fluorescence data from a test sample.Steps #4 and 5 include application of correction factors sFCDYE andIRESPONSE, which come from the factory/PM fluorescent dye cuvette test306 and the factory/PM tungsten source test 308 in FIG. 10.

The factory/PM tungsten source test 308 accounts for the wavelengthresponse of the collection optics for a given instrument unit. The testuses a quartz tungsten halogen lamp as a light source. Emission from thetungsten filament approximates a blackbody emitter. Planck's radiationlaw describes the radiation emitted into a hemisphere by a blackbody(BB) emitter:W _(BB)(λ)=[a·(CE)]/[λ⁵·{exp(b/λT)−1}]  (37)where a=2πhc²=3.742×10¹⁶ [W(nm)⁴/cm²]; b=hc/k=1.439×10⁷ [(nm)K]; T issource temperature; CE is a fitted parameter to account for collectionefficiency; and both T and CE are treated as variables determined for agiven tungsten lamp by curve-fitting emission data to Equation 37.

The lamp temperature, T, is determined by fitting NIST-traceable sourcedata to Equation 37. FIG. 23 shows a graph 582 depicting the spectralirradiance 584, W_(NIST lamp), of a NIST-traceablequartz-tungsten-halogen lamp, along with a curve fit 590 of the data tothe model in Equation 37 for blackbody irradiance, W_(BB). Since thelamp is a gray-body and not a perfect blackbody, Equation 37 includes aproportionality constant, CE. This proportionality constant alsoaccounts for the “collection efficiency” of the setup in an instrument102 as depicted in the tissue characterization system 100 of FIG. 1. Inthe illustrative embodiment, the target from which measurements areobtained is about 50-cm away from the lamp and has a finite collectioncone that subtends a portion of the emission hemisphere of the lamp.Thus, while W_(BB)(λ) in Equation 37 has units of [W/nm], calibrationvalues for a given lamp used in the instrument 102 in FIG. 1 has unitsof [W/cm²-nm at 50 cm distance]. The two calibration constants, CE andT, are obtained for a given lamp by measuring the intensity of the givenlamp relative to the intensity of a NIST-calibrated lamp using Equation38:W _(lamp) =[I _(lamp) /I _(NIST lamp) ]·W _(NIST lamp)  (38)Then, values of T and CE are determined by plotting W_(lamp) versuswavelength and curve-fitting using Equation 37. The curve fit provides acalibrated lamp response, I_(lamp)(λ), to which the tungsten lampresponse measured during factory/PM testing 308 at a given interrogationpoint and using a given instrument, S_(lamp)(i,λ), is compared. Thisprovides a measure of “instrument response”, IR(i,λ), for the givenpoint and the given instrument, as shown in Equation 39:IR(i,λ)=S _(lamp)(i,λ)/I _(lamp)(λ)  (39)

The factory/PM tungsten source test 308 in FIG. 10 includes collectingan intensity signal from the tungsten lamp as its light reflects off anapproximately 99% reflective target. The test avoids shadowing effectsby alternately positioning the tungsten source at each of twolocations—for example, on either side of the probe head 192 at locationscorresponding to the white light source locations 188, 190 shown in FIG.8—and using the data for each given interrogation point corresponding tothe source position where the given point is not in shadow.

Once the instrument response measure, IR(i,λ), is obtained, a correctionfactor is determined such that its value is normalized to unity at agiven wavelength, for example, at λ=500 nm. Thus, the distance betweenthe lamp and the detecting aperture, the photoelectron quantumefficiency of the detector, and the reflectivity of the target do notneed to be measured.

According to the illustrative embodiment, the fluorescence component ofthe spectral data pre-processing 114 of the system 100 of FIG. 1corrects a test fluorescence intensity signal, S_(F)(i,λ), forindividual instrument response by applying Equation 40 to produceI_(F)(i,λ), the instrument-response-corrected fluorescence signal:I _(F)(i,λ)=S _(F)(i,λ)÷[{500·IR(i,λ)}/{λ·IR(i,500)}]  (40)where IR(i,500) is the value of the instrument response measure IR atpoint i and at wavelength λ=500 nm; and where the term λ/500 convertsthe fluorescence intensity from energetic to photometric units,proportional to fluorophore concentration. In one embodiment, thedifferences between values of IR at different interrogation points issmall, and a mean of IR(λ) over all interrogation points is used inplace of IR(i,λ) in Equation 40.

The fluorescent dye cuvette test 306 accounts for variations in theefficiency of the collection optics 200 of a given instrument 102.Fluorescence collection efficiency depends on a number of factors,including the spectral response of the optics and detector used. In oneembodiment, for example, the collection efficiency tends to decreasewhen a scan approaches the edge of the optics. A fluorescent dye cuvettetest 306, performed as part of factory and/or preventive maintenance(PM) calibration, provides a means of accounting for efficiencydifferences.

An about 50-mm-diameter cuvette filled with a dye solution serves as atarget for the fluorescent dye cuvette test 306 to account forcollection optic efficiency variation with interrogation point positionand variation between different units. The factory/PM dye-filled cuvettetest 306 includes obtaining the peak intensity of the fluorescenceintensity signal at each interrogation point of the dye-filled cuvette,placed in the calibration target port of the instrument 102, andcomparing it to a mean peak intensity of the dye calculated for aplurality of units.

Illustratively, a calibrated dye cuvette can be prepared as follows.First, the fluorescence emission of a 10-mm-pathlength quartz cuvettefilled with ethylene glycol is obtained. The ethylene glycol is of 99+%spectrophotometric quality, such as that provided by Aldrich ChemicalCompany. The fluorescence emission reading is verified to be less thanabout 3000 counts, particularly at wavelengths near the dye peakintensity. An approximately 2.5×10⁻⁴ moles/L solution of coumarin-515 inethylene glycol is prepared. Coumarin-515 is a powdered dye of molecularweight 347, produced, for example, by Exciton Chemical Company. Thesolution is diluted with ethylene glycol to a final concentration ofabout 1.2×10⁻⁵ moles/L. Then, a second 10-mm-pathlength quartz cuvetteis filled with the coumarin-515 solution, and an emission spectrum isobtained. The fluorescence emission reading is verified to have amaximum between about 210,000 counts and about 250,000 counts. Thesolution is titrated with either ethylene glycol or concentratedcourmarin-515 solution until the peak lies in this range. Once achieved,50-mm-diameter quartz cuvettes are filled with the titrated standardsolution and flame-sealed.

A correction factor for fluorescence collection efficiency can bedetermined as follows. First, the value of fluorescence intensity of aninstrument-response-corrected signal, I_(F)(i,λ), is normalized by ameasure of the UV light energy delivered to the tissue as in Equation41:F _(T)(i,λ)=[I _(F)(i,λ)/P _(m)(i)]·[P _(m) /E _(μJ)]_(FC/PM)  (41)where F_(T)(i,λ) is the instrument-response-corrected,power-monitor-corrected fluorescence intensity signal; P_(m)(i) is apower-monitor reading that serves as an indirect measure of laserenergy, determined by integrating or adding intensity readings frompixels on a CCD array corresponding to a portion on which a beam of theoutput laser light is directed; and [P_(m)/E_(μJ)]_(FC/PM) is the ratioof power monitor reading to output laser energy determined duringfactory calibration and/or preventive maintenance (FC/PM).

Next, the illustrative embodiment includes obtaining the fluorescenceintensity response of a specific unit at a specific interrogation point(region) in its scan pattern using a cuvette of the titratedcoumarin-515 dye solution as the target, and comparing that response toa mean fluorescence intensity response calculated for a set of units,after accounting for laser energy variations as in Equation 41. Equation42 shows a fluorescence collection efficiency correction factor for agiven unit applied to an instrument-response-corrected fluorescencesignal, I_(F)(i,λ), along with the energy correction of Equation 41:

$\begin{matrix}\begin{matrix}{{F_{T}\left( {i,\lambda} \right)} = {\frac{I_{F}\left( {i,\lambda} \right)}{P_{m}(i)} \cdot \left( \frac{P_{m}}{E_{\mu\; J}} \right)_{PM} \cdot}} \\{\mspace{115mu}\left( \frac{\left\langle {\frac{I_{Dye}\left( {251,\lambda_{p}} \right)}{P_{m}(251)} \cdot \frac{P_{m}}{E_{uJ}}} \right\rangle_{Instruments}}{\frac{I_{Dye}\left( {i,\lambda_{p}} \right)}{P_{m}(i)} \cdot \frac{P_{m}}{E_{\mu\; J}}} \right)_{PM}}\end{matrix} & (42)\end{matrix}$where I_(Dye)(i,λ_(p)) is the peak measured fluorescence intensity atinterrogation position i using the dye-filled cuvette, as shown in FIG.31; λ_(p) is the wavelength (or its approximate pixel index equivalent)corresponding to the peak intensity; and the quantity in brackets <>_(Instruments) the mean power-corrected intensity at interrogationpoint 251, corresponding to the center of the exemplary scan pattern ofFIG. 5, calculated for a plurality of units.

The fluorescence collection efficiency tends to decrease when the scansapproach the edge of the optics. FIG. 24 shows typical fluorescencespectra from the dye test 306. The graph 614 in FIG. 24 depicts as afunction of wavelength 618 the fluorescence intensity 616 of the dyesolution at each region of a 499-point scan pattern. The curves 620 allhave approximately the same peak wavelength, λ_(p), but the maximumfluorescence intensity values vary.

FIG. 25 shows how the peak fluorescence intensity (intensity measured atpixel 131 corresponding approximately to λ_(p)) 624, determined in FIG.24, varies as a function of scan position (interrogation point) 626.Oscillations are due at least in part to optic scanning in thehorizontal plane, while the lower frequency frown pattern is due to scanstepping in the vertical plane. According to the illustrativeembodiment, curves of the fluorescence intensity of the dye cuvette atapproximate peak wavelength are averaged to improve on thesignal-to-noise ratio.

Equation 42 simplifies to Equations 43 and 44 as follows:

$\begin{matrix}{{F_{T}\left( {i,\lambda} \right)} = {\frac{I_{F}\left( {i,\lambda} \right)}{P_{m}(i)} \cdot \left( \frac{\left\langle {\frac{I_{Dye}\left( {251,\lambda_{p}} \right)}{P_{m}(251)} \cdot \frac{P_{m}}{E_{uJ}}} \right\rangle_{Instruments}}{\frac{I_{Dye}\left( {i,\lambda_{p}} \right)}{P_{m}(i)}} \right)_{PM}}} & (43) \\{\mspace{85mu}{= {\frac{I_{F}\left( {i,\lambda} \right)}{P_{m}(i)} \cdot {{FCDYE}(i)}}}} & (44)\end{matrix}$The term, [P_(m)/E_(μJ)]_(PM), drops out of equation 42. Variations inlaser energy measurements become less important as the energy isaveraged over multiple measurements made on many instruments.

In FIG. 10, the correction factor sFCDYE in block 318 is aone-dimensional scalar array and is calculated using Equation 45:

$\begin{matrix}{{sFCDYE} = \left( \frac{\left\langle {\frac{I_{Dye}\left( {251,\lambda_{p}} \right)}{P_{m}(251)} \cdot \frac{P_{m}}{E_{uJ}}} \right\rangle_{Instruments}}{\frac{I_{Dye}\left( {i,\lambda_{p}} \right)}{P_{m}(i)}} \right)} & (45)\end{matrix}$

Here, values of I_(Dye)(i,λ_(p)) are background-subtracted,power-corrected, and null-target-subtracted.

In FIG. 10, the correction factor IRESPONSE in block 320 is aone-dimensional array and is calculated using the results of thefactory/PM tungsten source test 308, as in Equation 46:IRESPONSE=[{500·IR(i,λ)}/{λ·IR(i,500)}]  (46)where IR(i,500) is the value of the instrument response measure IR givenin Equation 39 at point i and at wavelength λ=500 nm.

Steps #4 and 5 in block 346 of the fluorescence spectral datapre-processing block diagram 340 of FIG. 11 include processingfluorescence data using sFCDYE and IRESPONSE as defined in Equations 45and 46. The fluorescence data pre-processing proceeds bybackground-subtracting, power-correcting, and stray-light-subtractingfluorescence data from a test sample using Bkgnd[ ], sPowerMonitor[ ],and SLFL as shown in Steps #1, 2, and 3 in block 346 of FIG. 11. Then,the result is multiplied by sFCDYE and divided by IRESPONSE on apixel-by-pixel, location-by-location basis. Next, the resultingtwo-dimensional array is smoothed using a 5-point median filter, then asecond-order, 27-point Savitsky-Golay filter, and interpolated using thepixel-to-wavelength conversion determined in block 302 of FIG. 10 toproduce an array of data corresponding to a spectrum covering a rangefrom 360 nm to 720 nm at 1-nm intervals, for each of 499 interrogationpoints of the scan pattern.

As a further feature, the stability of fluorescence intensity readingsare monitored between preventive maintenance procedures. This may beperformed prior to each patient scan by measuring the fluorescenceintensity of the center plug 438 of the custom target 426 shown in FIG.19 and comparing the result to the expected value from the most recentpreventive maintenance test. If the variance from the expected value issignificant, and/or if the time between successive preventivemaintenance testing is greater than about a month, the followingcorrection factor may be added to those in block 346 of FIG. 11:

$\begin{matrix}{{FSTAB} = \frac{\left\lbrack \frac{I_{ct}\left( {251,\lambda_{p}} \right)}{P_{m}(251)} \right\rbrack_{PM}}{\left\lbrack \frac{I_{ct}\left( {251,\lambda_{p}} \right)}{P_{m}(251)} \right\rbrack_{PP}}} & (47)\end{matrix}$where PM denotes preventive maintenance test results; PP denotespre-patient test results; I_(ct)(251, λ_(p)) is the fluorescence peakintensity reading at scan position 251 (center of the custom target) atpeak wavelength λ_(p); and P_(m) is the power monitor reading at scanposition 251.

The spectral data pre-processing 114 in FIG. 11 further includes aprocedure for characterizing noise and/or applying a thresholdspecification for acceptable noise performance. Noise may be asignificant factor in fluorescence spectral data measurements,particularly where the peak fluorescence intensity is below about 20counts/μJ (here, and elsewhere in this specification, values expressedin terms of counts/μJ are interpretable in relation to the meanfluorescence of normal squamous tissue being 70 ct/μJ at about 450 nm).

The procedure for characterizing noise includes calculating a powerspectrum for a null target background measurement. The null targetbackground measurement uses a null target having about 0% reflectivity,and the measurement is obtained with internal lights off and optionallywith all external lights turned off so that room lights and othersources of stray light do not affect the measurement. Preferably, theprocedure includes calculating a mean null target background spectrum ofthe individual null target background spectra at all interrogationpoints on the target—for example, at all 499 points of the scan pattern202 of FIG. 5. Then, the procedure subtracts the mean spectrum from eachof the individual null target background spectra and calculates the FastFourier Transform (FFT) of each mean-subtracted spectrum. Then, a powerspectrum is calculated for each FFT spectrum and a mean power spectrumis obtained.

FIG. 26 shows a graph 678 depicting exemplary mean power spectra forvarious individual instruments 684, 686, 688, 690, 692, 694, 696. A27-point Savitzky-Golay filter has an approximate correspondingfrequency of about 6300 s⁻¹ and frequencies above about 20,000 s⁻¹ arerapidly damped by applying this filter. In the case of a 27-pointSavistzky-Golay filter, spectral data pre-processing in FIG. 11 furtherincludes applying a threshold maximum criterion of 1 count in the powerspectrum for frequencies below 20,000 s⁻¹. Here, data from an individualunit must not exhibit noise greater than 1 count at frequencies below20,000 s⁻¹ in order to satisfy the criterion. In FIG. 26, the criterionis not met for units with curves 692 and 696, since their power spectracontain points 706 and 708, each exceeding 1 count at frequencies below20,000 s⁻¹. The criterion is met for all other units.

According to an alternative illustrative embodiment, a second noisecriterion is applied instead of or in addition to the aforementionedcriterion. The second criterion specifies that the mean power spectralintensity for a given unit be below 1.5 counts at all frequencies. InFIG. 26, the criterion is not met for units with curves 692 and 696,since their power spectra contain points 700 and 702, each exceeding 1.5counts.

The illustrative spectral data pre-processing 114 in FIG. 11 and/or thefactory/PM 110 and pre-patient calibration 116 and correction in FIG. 10further includes applying one or more validation criteria to data fromthe factory/PM 110 and pre-patient 114 calibration tests. The validationcriteria identify possibly-corrupted calibration data so that the dataare not incorporated in the core classifier algorithms and/or thespectral masks of steps 132 and 130 in the system 100 of FIG. 1. Thevalidation criteria determine thresholds for acceptance of the resultsof the calibration tests. According to the illustrative embodiment, thesystem 100 of FIG. 1 signals if validation criteria are not met and/orprompts retaking of the data.

Validation includes validating the results of the factory/PM NIST 60%diffuse reflectance target test 314 in FIG. 10. Validation may benecessary, for example, because the intensity of the xenon lamp used inthe test 314 oscillates during a scan over the 25-mm scan pattern 202 ofFIG. 5. The depth of modulation of measured reflected light intensitydepends, for example, on the homogeneity of the illumination source atthe target, as well as the collection efficiency over the scan field.The depth of modulation also depends on how well the target is alignedrelative to the optical axis. In general, inhomogeneities of theillumination source are less important than inhomogeneities due totarget misalignment, since illumination source inhomogeneities aregenerally accounted for by taking the ratio of reflected light intensityto incident light intensity. Thus, the calibration 110, 116 methods useone or two metrics to sense off-center targets and prompt retaking ofdata.

One such metric includes calculating a coefficient of variation,CV_(i)(λ), of measured reflected light intensity across the scan fieldaccording to Equation 48:

$\begin{matrix}{{{CV}_{i}(\lambda)} = \frac{{{std}\left( {I\left( {\lambda,i} \right)} \right)}_{i}}{{{mean}\left( {I\left( {\lambda,i} \right)} \right)}_{i}}} & (48)\end{matrix}$where I(λ,i) mean[{I_(target)(λ,i)−I_(bkg)(λ,i)}/P_(m)(i)]_(4 rotations); “std”represents standard deviation; i represents an interrogation point; λrepresents wavelength (in one embodiment, between 370 nm and 700 nm);and P_(m)(i) represents the power monitor value for interrogation pointi. I(λ,i) is the mean of the background-subtracted (bkg),power-monitor-corrected reflectance intensity values from the NISTtarget measured 4 times, rotating the target 90° between eachmeasurement. Validation according to the metric of Equation 48 requiresthe value of CV_(i)(λ) be less than an experimentally-determined, fixedvalue.

Another metric from the 60% diffuse target test 314 includes calculatingthe relative difference, RD, between the minimum and maximum measuredintensity over the scan field according to Equation 49:

$\begin{matrix}\begin{matrix}{{{RD}(\lambda)} = \frac{2 \cdot \left\lbrack {{\max\left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i} - {\min\left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i}} \right\rbrack}{\left\lbrack {{\max\left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i} + {\min\left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i}} \right\rbrack}} \\{where} \\{{I^{\prime}\left( {\lambda,i} \right)} = {{{mean}\left( {\left( \frac{{I_{target}\left( {\lambda,i} \right)} - {I_{bkg}\left( {\lambda,i} \right)}}{P_{m}(i)} \right) \cdot {{mean}\left( {P_{m}(i)} \right)}_{i}} \right)}_{4\mspace{11mu}{rotations}}.}}\end{matrix} & (49)\end{matrix}$Here, I′ is scaled by the mean of the power monitor values. In oneembodiment, the relative difference, RD, between the minimum and maximumcomputed in Equation 49 is more sensitive to off-centered targets thanthe coefficient of variation, CV_(i), computed in Equation 48. Here,validation requires the value of RD(λ) be less than anexperimentally-determined, fixed value. In the illustrative embodiment,validation requires that Equation 50 be satisfied as follows:RD(λ)<0.7 for λ between 370 nm and 700 nm  (50)where RD(λ) is given by Equation 49.

Validation also includes validating the results of the tungsten sourcetest 308 from FIG. 11 using the approximately 99% diffuse reflectivitytarget. This test includes obtaining two sets of data, each setcorresponding to a different position of the external tungsten sourcelamp. Data from each set that are not affected by shadow are merged intoone set of data. Since the power monitor correction is not applicablefor this external source, a separate background measurement is obtained.

The illustrative calibration methods 110, 116 use one or two metrics tovalidate data from the tungsten source test 308. One metric includescalculating a coefficient of variation, CV_(i)(λ), of the meanforeground minus the mean background data, W(λ,i), of the merged set ofdata, as in Equation 51:

$\begin{matrix}{{{CV}_{i}(\lambda)} = \frac{{{std}\left( {W\left( {\lambda,i} \right)} \right)}_{i}}{{{mean}\left( {W\left( {\lambda,i} \right)} \right)}_{i}}} & (51)\end{matrix}$where the coefficient of variation, CV_(i)(λ), is calculated using themean instrument spectral response curve, IR, averaging over allinterrogation points of the scan pattern. Validation requires the valueof CV_(i)(λ) be less than an experimentally-determined, fixed value. Inthe illustrative embodiment, validation requires that Equation 52 besatisfied for all interrogation points i:CV_(i)(λ)<0.5 for λ between 370 nm and 700 nm  (52)where CV_(i)(λ) is given by Equation 51.

A second metric includes calculating a mean absolute differencespectrum, MAD(λ), comparing the current spectral response curve to thelast one measured, as in Equation 53:MAD (λ)=mean(|IR _(i)(i,λ)−IR _(i−1)(i,λ)|) _(i)  (53)where the instrument spectral response curve, IR, is given by Equation39. Validation requires the value of MAD(λ) be less than anexperimentally-determined, fixed value. In one embodiment, validationrequires that Equation 54 be satisfied:MAD(λ)<0.2 for λ between 370 nm and 700 nm  (54)where MAD(λ) is given by Equation 53.

Validation can further include validating the results of the fluorescentdye cuvette test 306 in FIG. 10, used to standardize fluorescencemeasurements between individual units and to correcting for variation incollection efficiency as a unit collects data at interrogation points ofa scan pattern. The illustrative calibration methods 110, 116 use one ormore metrics to validate data from the fluorescent dye cuvette test 306using a coefficient of variation, CV_(i)(λ), of dye cuvette intensity,I_(Dye), as in Equation 55:

$\begin{matrix}{{{CV}_{i}(\lambda)} = \frac{{{std}\left( {I_{Dye}\left( {\lambda,i} \right)} \right)}_{i}}{{{mean}\left( {I_{Dye}\left( {\lambda,i} \right)} \right)}_{i}}} & (55)\end{matrix}$

The coefficient of variation, CV_(i)(λ), in Equation 55 between about470 nm and about 600 nm is generally representative of fluorescenceefficiency variations over the scan pattern. The coefficient ofvariation at about 674 nm is a measure of how well the collection systemblocks the 337-nm excitation light. As the excitation light passes overthe surface of the cuvette, the incidence and collection angles go inand out of phase, causing modulation around 574 nm. The coefficient ofvariation at about 425 nm is a measure of the cleanliness of the cuvettesurface and is affected by the presence of fingerprints, for example.The coefficient of variation below about 400 nm and above about 700 nmis caused by a combination of the influence of 337-nm stray excitationlight and reduced signal-to-noise ratio due to limited fluorescence fromthe dye solution at these wavelengths.

One metric includes calculating a mean coefficient of variation,CV_(i)(λ), according to Equation 55, between about 500 nm and about 550nm, and comparing the mean coefficient of variation to anexperimentally-determined, fixed value. According to the illustrativeembodiment, validation requires that Equation 56 be satisfied:mean CV_(i(λ)<)0.06 for λ between 500 nm and 550 nm  (56)

A second metric includes requiring the coefficient of variation at about674 nm be less than an experimentally-determined, fixed value. In oneembodiment, validation requires that Equation 57 be satisfied for allinterrogation points i:CV_(i)(674)<0.5  (57)where CV_(i)(λ) is calculated as in Equation 55.

Validation can also include validating results of the fluorescent dyecuvette test 306 using both Equations 56 and 57. Here, applying Equation56 prevents use of data from tests where the scan axis is significantlyshifted relative to the center of the optical axis, as well as testswhere the cuvette is not full or is off-center. Applying Equation 57prevents use of data from tests where a faulty UV emission filter isinstalled or where the UV filter degrades over time, for example.

Validation can also include validating the results of the 10% diffusereflectivity custom target tests 312, 330 in FIG. 10. Here, anoff-center target may result in a faulty test due to interference atregions near the edge of the target, as well as regions near thefluorescent and phosphorescent plugs that are improperly masked.According to the illustrative embodiment, validation of the customtarget tests 312, 330 requires that the relative difference between theminimum and maximum intensity, RD(λ), is below a predetermined value,where RD(λ) is calculated as in Equation 58:

$\begin{matrix}{{{RD}(\lambda)} = \frac{2 \cdot \left\lbrack {{\max\left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i = {mask}} - {\min\left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i = {mask}}} \right\rbrack}{\left\lbrack {{\max\left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i = {mask}} + {\min\left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i = {mask}}} \right\rbrack}} & (58)\end{matrix}$where (I′(λ,i))_(i=mask) refers to all scan positions except thosemasked to avoid the plugs, as shown in FIGS. 19 and 20. In oneembodiment, validation requires that Equation 59 be satisfied:RD(λ)<1.2 for λ between 370 nm and 700 nm  (59)where RD(λ) is calculated as in Equation 58.

The invention can also validate the results of the null target test 304,328 in FIG. 10. The null target test is used, for example, to accountfor internal stray light in a given instrument. According to theillustrative embodiment, a maximum allowable overall amount of straylight is imposed. For example, in one preferred embodiment, validationof a null target test 304, 328 requires the integrated energy, IE, bebelow a predetermined value, where IE is calculated frombackground-subtracted, power-monitor-corrected null target reflectanceintensity measurements, as in Equation 60:

$\begin{matrix}\begin{matrix}{{IE} = {\int_{370}^{700}{{{{mean}\left( \frac{{{null}\left( {\lambda,i} \right)} - {{bkg}\left( {\lambda,i} \right)}}{P_{m}(i)} \right)}_{i} \cdot {{mean}\left( {P_{m}(i)} \right)}_{i}}{\mathbb{d}\lambda}}}} \\{\approx {\sum\limits_{370}^{700}{{{mean}\left( \frac{{{null}\left( {\lambda,i} \right)} - {{bkg}\left( {\lambda,i} \right)}}{P_{m}(i)} \right)}_{i} \cdot {{mean}\left( {P_{m}(i)} \right)}_{i}}}}\end{matrix} & (60)\end{matrix}$where Δλ in the summation above is about 1-nm. In one embodiment,validation requires that Equation 61 be satisfied:IE<4000 counts  (61)where IE is calculated as in Equation 60.

The invention may also employ validation of the open air target test 310in FIG. 10. Like the null target test 304, 328, the open air target testis used in accounting for internal stray light in a given instrument.According to the illustrative embodiment, validation of an open airtarget test 310 requires the integrated energy, IE, be below apredetermined value, where IE is calculated as in Equation 60, exceptusing open air reflectance intensity measurements in place of nulltarget measurements, null(λ,i). By way of example, in one casevalidation requires that the value of integrated energy for the open airtest be below 1.2 times the integrated energy from the null target test,calculated as in Equation 60.

According to another feature, the invention validates the power monitorcorrections used in the calibration tests in FIG. 10. Patient andcalibration data that use a power monitor correction may be erroneous ifthe illumination source misfires. According to one approach, validationof a power monitor correction requires that the maximum raw powermonitor intensity reading, P_(m,max)(i), be greater than a predeterminedminimum value and/or be less than a predetermined maximum value at eachinterrogation point i. In the illustrative embodiment, validationrequires that Equation 62 be satisfied:6000 counts<P _(m,max)(i)<30,000 counts for all i  (62)

According to the illustrative embodiment, spectral data pre-processing114 in FIG. 11 includes accounting for the result of the real-timemotion tracker 106 in the system 100 of FIG. 1 when applying thecorrection factors in block diagram 340 of FIG. 11. As discussed herein,the system 100 of FIG. 1 applies the calibration-based corrections inFIG. 11 to spectral data acquired from a patient scan. These correctionsare applied by matching spectral data from each interrogation point in apatient scan to calibration data from a corresponding interrogationpoint. However, a patient scan of the 499 interrogation points shown inthe scan pattern 202 of FIG. 5 takes approximately 12 seconds. Duringthose 12 seconds, it is possible that the tissue will shift slightly,due to patient movement. Thus, spectral data obtained during a scan maynot correspond to an initial index location, since the tissue has movedfrom its original position in relation to the scan pattern 202. Thereal-time motion tracker 106 of FIG. 1 accounts for this movement byusing data from video images of the tissue to calculate, as a functionof scan time, a translational shift in terms of an x-displacement and ay-displacement. The motion tracker 106 also validates the result bydetermining whether the calculated x,y translational shift accuratelyaccounts for movement of the tissue in relation to the scan pattern orsome other fixed standard such as the initial position of component(s)of the data acquisition system (the camera and/or spectroscope). Themotion tracker 106 is discussed in more detail below.

Illustratively, the spectral data pre-processing 114 in FIG. 11 accountsfor the result of the real-time motion tracker 106 by applying acalibration spectra lookup method. The lookup method includes obtainingthe motion-corrected x,y coordinates corresponding to the position ofthe center of an interrogation point from which patient spectral data isobtained during a patient scan. Then the lookup method includes usingthe x,y coordinates to find the calibration data obtained from aninterrogation point whose center is closest to the x,y coordinates.

The scan pattern 202 of FIG. 5 is a regular hexagonal sampling grid witha pitch (center-to-center distance) of 1.1 mm and a maximuminterrogation point spot size of 1 mm. This center-to-center geometryindicates a horizontal pitch of 1.1 mm, a vertical pitch of about 0.9527mm, and a maximum corner distance of the circumscribed regular hexagonto the center of 0.635 mm. Thus, the illustrative lookup method findsthe calibration interrogation point whose center is closest to themotion-corrected x,y coordinates of a patient scan interrogation pointby finding coordinates of a calibration point that is less than 0.635 mmfrom x,y.

The background spectra, Bkgnd[ ], in FIG. 11, are obtained at nearly thesame time patient spectral data are obtained and no motion correctionfactor is needed to background-subtract patient spectral data. Forexample, at a given interrogation point during a patient scan, thesystem 100 of FIG. 1 pulses the UV light source on only while obtainingfluorescence data, then pulses the BB1 light source on only whileobtaining the first set of reflectance data, then pulses the BB2 lightsource on only while obtaining the second set of reflectance data, thenobtains the background data, Bkgnd[ ], at the interrogation point withall internal light sources off. All of this data is considered to beapproximately simultaneous and no motion correction factor is needed forthe Bkgnd[ ] calibration data.

The real-time motion tracker 106 of FIG. 1 uses video data obtained fromthe tissue contemporaneously with the spectral data. In addition tomotion correction, the system of FIG. 1 uses video (image) data todetermine image masks for disease probability computation, to focus theprobe 142 through which spectral and/or image data is acquired, and tocompute a brightness and contrast correction and/or image enhancementfor use in disease overlay display.

Patient Scan Procedure

FIG. 27A is a block diagram 714 showing steps an operator performsbefore a patient scan as part of spectral data acquisition 104 in thesystem 100 of FIG. 1, according to an illustrative embodiment of theinvention. The steps in FIG. 27A are arranged sequentially with respectto a time axis 716. As shown, an operator applies a contrast agent tothe tissue sample 718, marks the time application is complete 720,focuses the probe 142 through which spectral and/or image data will beobtained 722, then initiates the spectral scan of the tissue 724 withina pre-determined window of time.

According to the illustrative embodiment, the window of time is anoptimum range of time following application of contrast agent to tissuewithin which an approximately 12 to 15 second scan can be performed toobtain spectral data that are used to classify tissue samples with ahigh degree of sensitivity and selectivity. The optimum window should belong enough to adequately allow for restarts indicated by focusingproblems or patient movement, but short enough so that the data obtainedis consistent. Consistency of test data is needed so that tissueclassification results for the test data are accurate and so that thetest data may be added to a bank of reference data used by the tissueclassification scheme. In one illustrative embodiment, the optimumwindow is expressed in terms of a fixed quantity of time followingapplication of contrast agent. In another illustrative embodiment, theoptimum window is expressed in terms of a threshold or range of atrigger signal from the tissue, such as a reflectance intensityindicative of degree of tissue whiteness.

The contrast agent in FIG. 27A is a solution of acetic acid. Accordingto one exemplary embodiment, the contrast agent is a solution betweenabout 3 volume percent and about 6 volume percent acetic acid in water.More particularly, in one preferred embodiment, the contrast agent is anabout 5 volume percent solution of acetic acid in water. Other contrastagents may be used, including, for example, formic acid, propionic acid,butyric acid, Lugol's iodine, Shiller's iodine, methylene blue,toluidine blue, indigo carmine, indocyanine green, fluorescein, andcombinations of these agents.

According to the illustrative embodiment, the time required to obtainresults from a patient scan, following pre-patient calibrationprocedures, is a maximum of about 5 minutes. Thus, in FIG. 27A, thefive-minute-or-less procedure includes applying acetic acid to thetissue sample 726; focusing the probe (142) 728; waiting, if necessary,for the beginning of the optimum pre-determined window of time forobtaining spectral data 730; obtaining spectral data at allinterrogation points of the tissue sample 732; and processing the datausing a tissue classification scheme to obtain a diagnostic display 734.The display shows, for example, a reference image of the tissue samplewith an overlay indicating regions that are classified as necrotictissue, indeterminate regions, healthy tissue (no evidence of disease,NED), and CIN 2/3 tissue, thereby indicating where biopsy may be needed.

The times indicated in FIG. 27A may vary. For example, if the real-timemotion tracker 106 in the system of FIG. 1 indicates too much movementoccurred during a scan 732, the scan 732 may be repeated if there issufficient time left in the optimum window.

FIG. 27B is a block diagram 738 showing a time line for the spectralscan 732 indicated in FIG. 27A. In the embodiment shown in FIG. 27B, ascan of all interrogation points of the scan pattern (for example, thescan pattern 202 of FIG. 5) takes from about 12 seconds to about 15seconds, during which time a sequence of images is obtained for motiontracking, as performed in step 106 of the system 100 of FIG. 1. By thetime a scan begins, a motion-tracking starting image 742 and a targetlaser image 744 have been obtained 740. The target laser image 744 maybe used for purposes of off-line focus evaluation, for example. Duringthe acquisition of spectral data during the scan, a frame grabber 120(FIG. 1) obtains a single image about once every second 746 for use inmonitoring and/or correcting for movement of the tissue from one frameto the next. In FIG. 27B, a frame grabber acquires images 748, 750, 752,754, 756, 758, 760, 762, 764, 766, 768 that are used to track motionthat occurs during the scan.

Image data from a video subsystem is used, for example, in targetfocusing 728 in FIG. 27A and in motion tracking 106, 746 in FIG. 27B.Image data is also used in detecting the proper alignment of a target ina calibration procedure, as well as detecting whether a disposable is inplace prior to contact of the probe with a patient. Additionally, in oneembodiment, colposcopic video allows a user to monitor the tissue samplethroughout the procedure.

Video Calibration and Focusing

FIG. 28 is a block diagram 770 that shows the architecture of anillustrative video subsystem used in the system 100 of FIG. 1. FIG. 28shows elements of the video subsystem in relation to components of thesystem 100 of FIG. 1. The video subsystem 770 acquires single videoimages and real-time (streaming) video images. The video subsystem 770can post-process acquired image data by applying a mask overlay and/orby adding other graphical annotations to the acquired image data.Illustratively, image data is acquired in two frame buffers duringreal-time video acquisition so that data acquisition and data processingcan be alternated between buffers. The camera(s) 772 in the videosubsystem 770 of FIG. 28 include a camera located in or near the probehead 192 shown in FIG. 4, and optionally includes a colposcope cameraexternal to the probe 142 for visual monitoring of the tissue sampleduring testing. In one illustrative embodiment, only the probe headcamera is used. FIG. 28 shows a hardware interface 774 between thecameras 772 and the rest of the video subsystem 770. The frame grabber120 shown in FIG. 1 acquires video data for processing in othercomponents of the tissue characterization system 100. In one embodiment,the frame grabber 120 uses a card for video data digitization (videocapture) and a card for broadband illumination (for example, flashlamps) control. For example, one embodiment uses a Matrox Meteor 2 cardfor digitization and an Imagenation PXC-200F card for illuminationcontrol, as shown in block 776 of FIG. 28.

Real-time (streaming) video images are used for focusing the probeoptics 778 as well as for visual colposcopic monitoring of the patient780. Single video images provide data for calibration 782, motiontracking 784, image mask computation (used in tissue classification)786, and, optionally, detection of the presence of a disposable 788. Insome illustrative embodiments, a single reference video image of thetissue sample is used to compute the image masks 108 in the system 100of FIG. 1. This reference image is also used in determining a brightnessand contrast correction and/or other visual enhancement 126, and is usedin the disease overlay display 138 in FIG. 1.

The illustrative video subsystem 770 acquires video data 790 from asingle video image within about 0.5 seconds. The video subsystem 770acquires single images in 24-bit RGB format and is able to convert themto grayscale images. For example, image mask computation 108 in FIG. 1converts the RGB color triplet data into a single luminance value, Y,(grayscale intensity value) at each pixel, where Y is given by Equation63:Y=0.299R+0.587G+0.114B  (63)where the grayscale intensity component, Y, is expressed in terms of red(R), green (G), and blue (B) intensities; and where R, G, and B rangefrom 0 to 255 for a 24-bit RGB image.

Laser target focusing 728 is part of the scan procedure in FIG. 27A. Anoperator uses a targeting laser in conjunction with real-time video toquickly align and focus the probe 142 prior to starting a patient scan.In the illustrative embodiment, an operator performs a laser “spot”focusing procedure in step 728 of FIG. 27A where the operator adjuststhe probe 142 to align laser spots projected onto the tissue sample. Theuser adjusts the probe while looking at a viewfinder with an overlayindicating the proper position of the laser spots. In one alternativeembodiment, an operator instead performs a thin-line laser focusingmethod, where the operator adjusts the probe until the laser linesbecome sufficiently thin. The spot focus method allows for faster, moreaccurate focusing than a line-width-based focusing procedure, since thinlaser lines can be difficult to detect on tissue, particularly darktissue or tissue obscured by blood. Quick focusing is needed in order toobtain a scan within the optimal time window following application ofcontrast agent to the tissue; thus, a spot-based laser focusing methodis preferable to a thin line method, although a thin line focus methodmay be used in alternative embodiments.

A target focus validation procedure 122 is part of the tissuecharacterization system 100 of FIG. 1, and determines whether theoptical system of the instrument 102 is in focus prior to a patientscan. If the system is not in proper focus, the acquired fluorescenceand reflectance spectra may be erroneous. Achieving proper focus isimportant to the integrity of the image masking 108, real-time tracking106, and overall tissue classification 132 components of the system 100of FIG. 1.

The focus system includes one or more target laser(s) that project laserlight onto the patient sample prior to a scan. In one embodiment, thetargeting laser(s) project laser light from the probe head 192 towardthe sample at a slight angle with respect to the optical axis of theprobe 142 so that the laser light that strikes the sample moves withinthe image frame when the probe is moved with respect to the focal plane.For example, in one illustrative embodiment, four laser spots aredirected onto a target such that when the probe 142 moves toward thetarget during focusing, the spots move closer together, toward thecenter of the image. Similarly, when the probe 142 moves away from thetarget, the spots move further apart within the image frame, toward thecorners of the image.

FIG. 29A is a single video image 794 of a target 796 of 10% diffusereflectivity upon which a target laser projects a focusing pattern offour laser spots 798, 800, 802, 804. During laser target focusing 728(FIG. 27A), an operator views four focus rings that are displayed atpredetermined locations, superimposed on the target focusing image. FIG.29B depicts the focusing image 794 on the target 796 in FIG. 29A withsuperimposed focus rings 806, 808, 810, 812. The operator visuallyexamines the relative positions of the laser spots 798, 800, 802, 804 inrelation to the corresponding focus rings 806, 808, 810, 812 whilemoving the probe head 192 along the optical axis toward or away from thetarget/tissue sample. When the laser spots lie within the focus rings asshown in FIG. 29B, the system is within its required focus range. Thebest focus is achieved by aligning the centers of all the laser spotswith the corresponding centers of the focus rings. Alternatively, spotpatterns of one, two, three, five, or more laser spots may be used forfocus alignment.

It is generally more difficult to align laser spots that strike anon-flat tissue sample target than to align the spots on a flat, uniformtarget as shown in FIG. 29B. In some instances, a laser spot projectedonto tissue is unclear, indistinct, or invisible. Visual evaluation offocus may be subjective and qualitative. Thus, a target focus validationprocedure is useful to insure proper focus of a tissue target isachieved. Proper focus allows the comparison of both image data andspectral data from different instrument units and different operators.

In one illustrative embodiment, the system 100 of FIG. 1 performs anautomatic target focus validation procedure using a single focus image.The focus image is a 24-bit RGB color image that is obtained beforeacquisition of spectral data in a patient scan. The focus image isobtained with the targeting laser turned on and the broadband lights(white lights) turned off. Automatic target focus validation includesdetecting the locations of the centers of visible laser spots andmeasuring their positions relative to stored, calibrated positions(“nominal” center positions). Then, the validation procedure applies adecision rule based on the number of visible laser spots and theirpositions and decides whether the system is in focus and a spectral scancan be started.

FIG. 30 is a block diagram 816 of a target focus validation procedureaccording to an illustrative embodiment of the invention. The stepsinclude obtaining a 24-bit RGB focus image 818, performing imageenhancement 820 to highlight the coloration of the laser spots,performing morphological image processing (dilation) to fill holes andgaps within the spots 822, defining a region of interest (ROI) of theimage 824, and computing a mean and standard deviation 826 of theluminance values (brightness) of pixels within the region of interest.Next, the focus validation procedure iteratively and dynamicallythresholds 828 the enhanced focus image using the computed mean andstandard deviation to extract the laser spots. Between thresholdingiterations, morphological processing 830 disconnects differentiatedimage objects and removes small image objects from the thresholdedbinary image, while a region analysis procedure 832 identifies andremoves image objects located outside the bounds of the target laserspot pathways 838 and objects whose size and/or shape do not correspondto a target laser spot. After all thresholding iterations, the found“spots” are either verified as true target laser spots or are removedfrom the image 834, based on size, shape, and/or location. Next, in step842, the focus validation procedure computes how far the centers of thefound spots are from the nominal focus centers and converts thedifference from pixels to millimeters in step 844. The validationprocedure then applies a decision rule based on the number of foundspots and their positions and decides whether the system is in focussuch that a spectral scan of the patient can begin.

The focus validation procedure of FIG. 30 begins with obtaining the24-bit RGB focus image and splitting it into R, G, and B channels. Eachchannel has a value in the range of 0 to 255. FIG. 31 depicts the RGBfocus image 794 from FIG. 29A with certain illustrative geometrysuperimposed. FIG. 31 shows the four nominal spot focus centers 850,852, 854, 856 as red dots, one of which is the red dot labeled “N” inquadrant 1. The nominal spot focus centers represent the ideal locationof centers of the projected laser spots, achieved when the probe opticsare in optimum focus. The nominal spot focus centers 850, 852, 854, 856correspond to the centers of the rings 806, 808, 810, 812 in FIG. 29B.An (x,y) position is determined for each nominal focus center. A nominalimage focus center (857), O, is defined by the intersection of the twored diagonal lines 858, 860 in FIG. 31. The red diagonal lines 858, 860connect the two pairs of nominal spot focus centers 852, 854 inquadrants 2 and 3 and 850, 856 in quadrants 1 and 4, respectively. Also,the slopes of the two lines 858, 860 are computed for later use.

Step 820 in the procedure of FIG. 30 is image enhancement to highlightthe coloration of the laser spots in contrast to the surrounding tissue.In one embodiment, the R value of saturated spots is “red clipped” suchthat if R is greater than 180 at any pixel, the R value is reduced by50. Then, a measure of greenness, G_(E), of each pixel is computed as inEquation 64:G _(E) =G−R−15  (64)where G is the green value of a pixel, R is the red value of the pixel,and 15 is a correction factor to remove low intensity noise,experimentally-determined here to be 15 gray levels.

FIG. 32A represents the green channel of an RGB image 864 of a cervicaltissue sample, used in an exemplary target focus validation procedure.In this image, only two top focus laser spots 868, 870 are clear. Thelower right spot 872 is blurred/diffused while the lower left spot 874is obscured. The green-channel luminance (brightness), G_(E), of thegreen-enhanced RGB image 864 of FIG. 32A may be computed using Equation64 and may be displayed, for example, as grayscale luminance valuesbetween 0 and 255 at each pixel.

In step 822 of FIG. 30, the focus validation procedure performsmorphological dilation using a 3×3 square structuring element to fillholes and gaps within the found spots. Then in step 824, the procedureuses a pre-defined, circular region of interest (ROI) for computing amean, M, and a standard deviation, STD, 826 of the greenness value,G_(E), of the pixels within the ROI, which are used in iterative dynamicthresholding 828. According to the illustrative embodiment, the ROI is asubstantially circular region with a 460-pixel diameter whose centercoincides with the nominal image focus center, O.

Before iterative dynamic thresholding begins, G_(E) is set equal to zeroat a 50-pixel diameter border about the ROI. Then, iterative dynamicthresholding 828 begins by setting an iteration variable, p, to zero,then computing a threshold value, Th, as follows:Th=M+p·STD  (65)where M and STD are defined from the ROI. Since p=0 in the firstiteration, the threshold, Th, is a “mean” greenness value over theentire ROI in the first iteration. In this embodiment, imagethresholding is a subclass of image segmentation that divides an imageinto two segments. The result is a binary image made up of pixels, eachpixel having a value of either 0 (off) or 1 (on). In step 828 of thefocus validation procedure of FIG. 30, the enhanced greenness value of apixel corresponding to point (x,y), within the ROI, G_(E)(x,y), iscompared to the threshold value, Th. The threshold is applied as inEquation 66:IF G_(E)(x,y)>Th, THEN the binary pixel value at (x,y), B_(T)=1, elseB_(T)=0.  (66)

Iterative dynamic thresholding 828 proceeds by performing morphologicalopening 830 to separate nearby distinguishable image objects and toremove small objects of the newly thresholded binary image. According tothe illustrative embodiment, the morphological opening 830 includesperforming an erosion, followed by a dilation, each using a 3×3 squarestructuring element. The procedure then determines the centroid of eachof the thresholded objects and removes each object whose center isoutside the diagonal bands bounded by two lines that are 40 pixels aboveand below the diagonal lines 858, 860 in FIG. 31. These diagonal bandsinclude the region between lines 876, 878 and the region between lines880, 882 in FIG. 31, determined in step 838 of FIG. 30. An image objectwhose center lies outside these bands does not correspond to a targetfocus spot, since the centers of the focus laser spots should appearwithin these bands at any position of the probe along the optical axis.The spots move closer together, within the bands, as the probe movescloser to the tissue sample, and the spots move farther apart, withinthe bands, as the probe moves away from the tissue sample.

Next, step 832 of the thresholding iteration 828 computes an area (A),eccentricity (E), and equivalent diameter (ED) of the found imageobjects, and removes an object whose size and/or shape—described here byA, E, and ED—does not correspond to that of a focus laser spot. E and EDare defined as follows:E=(1−b ² /a ²)^(0.5)  (67)ED=2(A/π)^(0.5)  (68)where a is the minor axis length and b is the major axis length in unitsof pixels. For example, step 832 applies Equation 69 as follows:IF A>5000 OR IF E>0.99 OR IF ED>110, THEN remove object (set B_(T)=0 forall pixels in object).  (69)Other criteria may be applied. For example, Equation 70 may be appliedin place of Equation 69:IF A>2500 OR IF E>0.99 OR IF ED>80, THEN remove object (set B_(T)=0 forall pixels in object).  (70)

Next, the iteration variable, p, is increased by a fixed value, forexample, by 0.8, and a new threshold is calculated using Equation 65.The iteration proceeds by applying the new threshold, performing amorphological opening, computing centroids of the newly thresholdedregions, removing regions whose center position, size, and/or shape donot correspond to those of a target focus spot, and stepping up thevalue of the iteration variable p. Iterative dynamic thresholdingproceeds until a pre-determined condition is satisfied. For example, thethresholding ends when the following condition is satisfied:IF p>6 OR IF the number of qualified spots (image objects)≦4, THENSTOP.  (71)

Step 834 of the focus validation procedure eliminates any image objectremaining after dynamic thresholding that does not meet certain laserspot size and shape criteria. For example, according to the illustrativeembodiment, step 834 applies the condition in Equation 72 for eachremaining image object:IF A<80 OR IF E>0.85 OR IF ED<10, THEN remove object.  (72)

In an alternative embodiment, one or more additional criteria based onthe position of each image object (found spot) are applied to eliminateobjects that are still within the focus bands of FIG. 31, but are toofar from the nominal centers 850, 852, 854, 856 to be valid focus spots.

FIG. 32B shows an image 898 of the cervical tissue sample of FIG. 32Afollowing step 834, wherein the top two image objects were verified astarget laser spots, while the bottom objects were eliminated.

Step 842 of the focus validation procedure assigns each of the foundspots to its respective quadrant and computes the centroid of each foundspot. FIG. 31 shows the found spots as blue dots 900, 902, 904, 906.Then for each found spot, step 842 computes the distance between thecenter of the spot to the nominal image focus center 857, O. For thefocus spot center 900 labeled “F” in FIG. 31, this distance is L_(OF),the length of the blue line 910 from point O to point F. The distancebetween the nominal focus center, N, 850 corresponding to the quadrantcontaining the found spot, and the nominal image focus center 857, O, isL_(ON), the length of the red line 912 from point O to point N. Step 842of the focus validation procedure then determines a focus value forverified focus spot 900 equal to the difference between the lengthsL_(OF) and L_(ON). The focus value of each of the verified focus spotsis computed in this manner, and the focus values are converted frompixels to millimeters along the focus axis (z-axis) in step 844 of FIG.30 using an empirically-determined conversion ratio—for example, 0.34 mmper pixel.

Next, the focus validation procedure of FIG. 30 applies a decision rulein step 846 based on the number of found spots and their positions. Thedecision rule is a quantitative means of deciding whether the system isin focus and a spectral scan of the tissue can begin. According to theillustrative embodiment, step 846 applies a decision rule given byEquations 73, 74, and 75:IF 3 or more spots are found, THEN IF the focus value determined in step842 is ≦6 mm for any 3 spots OR IF the focus value is ≦4 mm for any 2spots, THEN “Pass”, ELSE “Fail” (require refocus).  (73)IF only 2 spots are found, THEN IF the focus value of any spot is ≧4 mm,THEN “Fail” (require refocus), ELSE “Pass”.  (74)IF ≦1 spot is found, THEN “Fail” (require refocus).  (75)Other decision rules may be used alternatively.

FIGS. 33 and 34 show the application of the focus validation procedureof FIG. 30 using a rubber cervix model placed so that the two upperlaser spots are within the os region. For this example, the distancebetween the edge of the probe head 192 and the target (or target tissue)is approximately 100 mm at optimum focus, and the distance light travelsbetween the target (or target tissue) and the first optic within theprobe 142 is approximately 130 mm at optimum focus.

FIG. 33 is a 24-bit RGB target laser focus image 942 of a rubber cervixmodel 944 onto which four laser spots 946, 948, 950, 952 are projected.The cervix model 944 is off-center in the image 942 such that the twoupper laser spots 946, 948 lie within the os region. FIG. 34 shows agraph 954 depicting as a function of probe position relative to thetarget tissue 956, the mean of a focus value 958 (in pixels) of each ofthe four laser spots 946, 948, 950, 952 projected onto the rubber cervixmodel 944. The curve fit 960 of the data indicates the relationshipbetween measured focus, f, 958 and probe location, z_(p), 956 (in mm) issubstantially linear. However, the curve is shifted down and is notcentered at (0,0). This indicates a focus error introduced by the manualalignment used to obtain the z=0 focus position. Such an error mayprompt a “Fail” determination in step 846 of the focus validationprocedure of FIG. 30, depending on the chosen decision rule. FIG. 34indicates the difficulty in making a visual focus judgment to balancethe focus of the four spots, particularly where the target surface(tissue sample) is not flat and perpendicular to the optical axis(z-axis) of the probe system.

The focus validation procedure illustrated in FIG. 30 provides anautomatic, quantitative check of the quality of focus. Additionally, inthe illustrative embodiment, the focus validation procedure predicts theposition of optimum focus and/or automatically focuses the opticalsystem accordingly by, for example, triggering a galvanometer subsystemto move the probe to the predicted position of optimum focus.

The focus validation procedure in FIG. 30 produces a final decision instep 846 of “Pass” or “Fail” for a given focus image, based on thedecision rule given by Equations 73–75. This indicates whether the focusachieved for this tissue sample is satisfactory and whether a spectraldata scan may proceed as shown in step 732 of FIGS. 27A and 27B.

Determining Optimal Data Acquisition Window

After application of contrast agent 726 and target focusing 728, step730 of FIG. 27A indicates that the operator waits for the beginning ofthe optimum window for obtaining spectral data unless the elapsed timealready exceeds the start of the window. The optimum window indicatesthe best time period for obtaining spectral data, following applicationof contrast agent to the tissue, considering the general timeconstraints of the entire scan process in a given embodiment. Forexample, according to the illustrative embodiment, it takes from about12 to about 15 seconds to perform a spectral scan of 499 interrogationpoints of a tissue sample. An optimum window is determined such thatdata may be obtained over a span of time within this window from asufficient number of tissue regions to provide an adequately detailedindication of disease state with sufficient sensitivity and selectivity.The optimum window preferably, also allows the test data to be used, inturn, as reference data in a subsequently developed tissueclassification module. According to another feature, the optimum windowis wide enough to allow for restarts necessitated, for example, byfocusing problems or patient movement. Data obtained within the optimumwindow can be added to a bank of reference data used by a tissueclassification scheme, such as component 132 of the system 100 ofFIG. 1. Thus, the optimum window is preferably narrow enough so thatdata from a given region is sufficiently consistent regardless of when,within the optimum window, it is obtained.

According to the illustrative embodiment, the optimal window forobtaining spectral data in step 104 of FIG. 1 is a period of time fromabout 30 seconds following application of the contrast agent to about130 seconds following application of the contrast agent. The time ittakes an operator to apply contrast agent to the tissue sample may vary,but is preferably between about 5 seconds and about 10 seconds. Theoperator creates a time stamp in the illustrative scan procedure of FIG.27A after completing application of the contrast agent, and then waits30 seconds before a scan may begin, where the optimum window is betweenabout 30 seconds and about 130 seconds following application of contrastagent. If the scan takes from about 12 seconds to about 15 seconds tocomplete (where no retake is required), the start of the scan proceduremust begin soon enough to allow all the data to be obtained within theoptimum window. In other words, in this embodiment, the scan must beginat least before 115 (assuming a worst case of 15 seconds to complete thescan) seconds following the time stamp (115 seconds after application ofcontrast agent) so that the scan is completed by 130 seconds followingapplication of contrast agent. Other optimum windows may be used. In oneembodiment, the optimum window is between about 30 seconds and about 110seconds following application of contrast agent. One alternativeembodiment has an optimal window with a “start” time from about 10 toabout 60 seconds following application of acetic acid, and an “end” timefrom about 110 to about 180 seconds following application of aceticacid. Other optimum windows may be used.

In one illustrative embodiment, the tissue characterization system 100of FIG. 1 includes identifying an optimal window for a givenapplication, and/or subsequently using spectral data obtained within thepre-determined window in a tissue classification module, such as step132 of FIG. 1. According to one feature, optimal windows are determinedby obtaining optical signals from reference tissue samples with knownstates of health at various times following application of a contrastagent.

Determining an optimal window illustratively includes the steps ofobtaining a first set of optical signals from tissue samples having aknown disease state, such as CIN 2/3 (grades 2 and/or 3 cervicalintraepithelial neoplasia); obtaining a second set of optical signalsfrom tissue samples having a different state of health, such as non-CIN2/3; and categorizing each optical signal into “bins” according to thetime it was obtained in relation to the time of application of contrastagent. The optical signal may include, for example, a reflectancespectrum, a fluorescence spectrum, a video image intensity signal, orany combination of these.

A measure of the difference between the optical signals associated withthe two types of tissue is then obtained, for example, by determining amean signal as a function of wavelength for each of the two types oftissue samples for each time bin, and using a discrimination function todetermine a weighted measure of difference between the two mean opticalsignals obtained within a given time bin. This provides a measure of thedifference between the mean optical signals of the two categories oftissue samples—diseased and healthy—weighted by the variance betweenoptical signals of samples within each of the two categories.

According to the illustrative embodiment, the invention further includesdeveloping a classification model for each time bin for the purpose ofdetermining an optimal window for obtaining spectral data in step 104 ofFIG. 1. After determining a measure of difference between the tissuetypes in each bin, an optimal window of time for differentiating betweentissue types is determined by identifying at least one bin in which themeasure of difference between the two tissue types is substantiallymaximized. For example, an optimal window of time may be chosen toinclude every time bin in which a respective classification modelprovides an accuracy of 70% or greater. Here, the optimal windowdescribes a period of time following application of a contrast agent inwhich an optical signal can be obtained for purposes of classifying thestate of health of the tissue sample with an accuracy of at least 70%.Models distinguishing between three or more categories of tissue mayalso be used in determining an optimal window for obtaining spectraldata. As discussed below, other factors may also be considered indetermining the optimal window.

An analogous embodiment includes determining an optimal threshold orrange of a measure of change of an optical signal to use in obtaining(or triggering the acquisition of) the same or a different signal forpredicting the state of health of the sample. Instead of determining aspecific, fixed window of time, this embodiment includes determining anoptimal threshold of change in a signal, such as a video image whitenessintensity signal, after which an optical signal, such as a diffusereflectance spectrum and/or a fluorescence spectrum, can be obtained toaccurately characterize the state of health or other characteristic ofthe sample. This illustrative embodiment includes monitoring reflectanceand/or fluorescence at a single or multiple wavelength(s), and uponreaching a threshold change from the initial condition, obtaining a fullreflectance and/or fluorescence spectrum for use in diagnosing theregion of tissue. This method allows for reduced data retrieval andmonitoring, since it involves continuous tracking of a single,partial-spectrum or discrete-wavelength “trigger” signal (instead ofmultiple, full-spectrum scans), followed by the acquisition of spectraldata in a spectral scan for use in tissue characterization, for example,the tissue classification module 132 of FIG. 1. Alternatively, thetrigger may include more than one discrete-wavelength orpartial-spectrum signal. The measure of change used to trigger obtainingone or more optical signals for tissue classification may be a weightedmeasure, and/or it may be a combination of measures of change of morethan one signal.

In a further illustrative embodiment, instead of determining an optimalthreshold or range of a measure of change of an optical signal, anoptimal threshold or range of a measure of the rate of change of anoptical signal is determined. For example, the rate of change ofreflectance and/or fluorescence is monitored at a single or multiplewavelength(s), and upon reaching a threshold rate of change, a spectralscan is performed to provide spectral data for use in diagnosing theregion of tissue. The measure of rate of change used to triggerobtaining one or more optical signals for tissue classification may be aweighted measure, and/or it may be a combination of measures of changeof more than one signal. For example, the measured rate of change may beweighted by an initial signal intensity.

According to the illustrative embodiment, the optimum time windowincludes a time window in which spectra from cervical tissue may beobtained such that sites indicative of grades 2 and 3 cervicalintraepithelial neoplasia (CIN 2/3) can be separated from non-CIN 2/3sites. Non-CIN 2/3 sites include sites with grade 1 cervicalintraepithelial neoplasia (CIN 1), as well as NED sites, normal columnarand normal squamous epithelia, and mature and immature metaplasia.Alternately, sites indicative of high grade disease, CIN 2+, whichincludes CIN 2/3 categories, carcinoma in situ (CIS), and cancer, may beseparated from non-high-grade-disease sites. In general, for anyembodiment discussed herein in which CIN 2/3 is used as a category forclassification or characterization of tissue, the more expansivecategory CIN 2+ may be used alternatively. Preferably, the system 100can differentiate amongst three or more classification categories.Exemplary embodiments are described below and include analysis of thetime response of diffuse reflectance and/or 337-nm fluorescence spectraof a set of reference tissue samples with regions having known states ofhealth to determine temporal characteristics indicative of therespective states of health. These characteristics are then used inbuilding a model to determine a state of health of an unknown tissuesample. Other illustrative embodiments include analysis of fluorescencespectra using other excitation wavelengths, such as 380 nm and 460 nm,for example.

According to one illustrative embodiment, an optimum window isdetermined by tracking the difference between spectral data of twotissue types using a discrimination function.

According to the illustrative embodiment, the discrimination functionshown below in Equation 76 may be used to extract differences betweentissue types:

$\begin{matrix}{{D(\lambda)} = \frac{{\mu\left( {{test}(\lambda)} \right)}_{{non}\text{-}{CIN}\mspace{11mu}{2/3}} - {\mu\left( {{test}(\lambda)} \right)}_{{CIN}\mspace{11mu}{2/3}}}{\sqrt{{\sigma^{2}\left( {{test}(\lambda)} \right)}_{{non}\text{-}{CIN}\mspace{11mu}{2/3}} + {\sigma^{2}\left( {{test}(\lambda)} \right)}_{{CIN}\mspace{11mu}{2/3}}}}} & (76)\end{matrix}$where μ corresponds to the mean optical signal for the tissue typeindicated in the subscript; and σ corresponds to the standard deviation.The categories CIN 2/3 and non-CIN 2/3 are used in this embodimentbecause spectral data is particularly well-suited for differentiatingbetween these two categories of tissue, and because spectral data isprominently used in one embodiment of the classification schema in thetissue classification module in step 132 of FIG. 1 to identify CIN 2/3tissue. Thus, in this way, it is possible to tailor the choice of anoptimal scan window such that spectral data obtained within that windoware well-adapted for use in identifying CIN 2/3 tissue in the tissueclassification scheme 132. In one illustrative embodiment, the opticalsignal in Equation 76 includes diffuse reflectance. In anotherillustrative embodiment, the optical signal includes 337-nm fluorescenceemission spectra. Other illustrative embodiments use fluorescenceemission spectra at another excitation wavelength such as 380 nm and 460nm. In still other illustrative embodiments, the optical signal is avideo signal, Raman signal, or infrared signal. Some illustrativeembodiments include using difference spectra calculated betweendifferent phases of acetowhitening, using various normalization schema,and/or using various combinations of spectral data and/or image data asdiscussed above.

In one preferred embodiment, determining an optimal window for obtainingspectral data in step 104 of FIG. 1 includes developing lineardiscriminant analysis models using spectra from each time bin shown inTable 1 below.

TABLE 1 Time bins for which means spectra are obtained in an exemplaryembodiment Bin Time after application of Acetic Acid (s) 1 t ≦ 0  2  0 <t ≦ 40 3 40 < t ≦ 60 4 60 < t ≦ 80 5  80 < t ≦ 100 6 100 < t ≦ 120 7 120< t ≦ 140 8 140 < t ≦ 160 9 160 < t ≦ 180 10 t > 180

Alternatively, nonlinear discriminant analysis models may be developed.Generally, models for the determination of an optimal window are trainedusing reflectance and fluorescence data separately, although someembodiments include using both data types to train a model. Thediscriminant analysis models discussed herein for exemplary embodimentsof the determination of an optimal window are generally lesssophisticated than the schema used in the tissue classification module132 in FIG. 1. Alternatively, a model based on the tissue classificationschema in the module 132 in FIG. 1 can be used to determine an optimalwindow for obtaining spectral data in step 104 of FIG. 1.

In the exemplary embodiments for determining an optimal window discussedherein, reflectance and flurorescence intensities are down-sampled toone value every 10 nm between 360 and 720 nm. A model is trained byadding and removing intensities in a forward manner, continuouslyrepeating the process until the model converges such that additionalintensities do not appreciably improve tissue classification. Testing isperformed by a leave-one-spectrum-out jack-knife process.

Apr. 17, 2003 FIG. 35 shows the difference between the mean reflectancespectra for non-CIN 2/3 tissues and CIN 2/3 tissues at three times(prior to the application of acetic acid (graph 976), maximum whitening(graph 978, about 60–80 seconds post-AA), and the last time data wereobtained (graph 980, about 160–180 seconds post-AA)). The timecorresponding to maximum whitening was determined from reflectance data,and occurs between about 60 seconds and 80 seconds following applicationof acetic acid. In the absence of acetic acid, the reflectance spectrafor CIN 2/3 (curve 982 of graph 976 in FIG. 35) are on average lowerthan non-CIN 2/3 tissue (curve 984 of graph 976 in FIG. 35). Followingthe application of acetic acid, a reversal is noted—CIN 2/3 tissues havehigher reflectance than the non-CIN 2/3 tissues. The reflectance of CIN2/3 and non-CIN 2/3 tissues increase with acetic acid, with CIN 2/3showing a larger relative percent change (compare curves 986 and 988 ofgraph 978 in FIG. 35). From about 160 s to about 180 s following aceticacid, the reflectance of CIN 2/3 tissue begins to return to thepre-acetic acid state, while the reflectance of the non-CIN 2/3 groupcontinues to increase (compare curves 990 and 992 of graph 980 in FIG.35)

Discrimination function ‘spectra’ are calculated from the reflectancespectra of CIN 2/3 and non-CIN 2/3 tissues shown in FIG. 35 as one wayto determine an optimal window for obtaining spectral data.Discrimination function spectra comprise values of the discriminationfunction in Equation 76 determined as a function of wavelength for setsof spectral data obtained at various times. As shown in FIG. 36, thelargest differences (measured by the largest absolute values ofdiscrimination function) are found about 60 s to about 80 s post-aceticacid (curve 1002), and these data agree with the differences seen in themean reflectance spectra of FIG. 35 (curves 986 and 988 of graph 978 inFIG. 35).

Multivariate linear regression analysis takes into account wavelengthinterdependencies in determining an optimal data acquisition window. Oneway to do this is to classify spectral data shown in FIG. 35 using amodel developed from the reflectance data for each of the bins inTable 1. Then, the accuracy of the models for each bin is computed andcompared. Reflectance intensities are down-sampled to one about every 10nm between about 360 nm and about 720 nm. The model is trained by addingintensities in a forward-stepped manner. Testing is performed with aleave-one-spectrum-out jack-knife process. The results of the linearregression show which wavelengths best separate CIN 2/3 from non-CIN2/3, as shown in Table 2.

TABLE 2 Forwarded selected best reflectance wavelengths for classifyingCIN 2/3 from non-CIN 2/3 spectra obtained at different times pre andpost-AA. Time from AA (s) LDA Model Input Wavelengths (nm) Accuracy −30370 400 420 440 530 570 590 610 66 30 420 430 450 600 74 50 360 400 420430 580 600 74 70 360 370 420 430 560 580 600 77 90 360 420 430 540 59073 110 360 440 530 540 590 71 130 360 420 430 540 590 71 150 370 400 430440 540 620 660 690 720 72 170 490 530 570 630 650 75

As shown in Table 2, the two best models for separating CIN 2/3 andnon-CIN 2/3, taking into account wavelength interdependence, usereflectance data obtained at peak CIN 2/3 whitening (from about 60 s toabout 80 s) and reflectance data obtained from about 160 s to about 180s post acetic acid. The first model uses input wavelengths between about360 and about 600 nm, while the second model uses more red-shiftedwavelengths between about 490 and about 650 nm. This analysis shows thatthe optimal windows are about 60 s–80 s post AA and about 160–180 postAA (the latest time bin). This is consistent with the behavior of thediscrimination function spectra shown in FIG. 6.

FIG. 37 demonstrates one step in determining an optimal window forobtaining spectral data, for purposes of discriminating between CIN 2/3and non-CIN 2/3 tissue. FIG. 37 shows a graph 1006 depicting theperformance of the two LDA models described in Table 2 above as appliedto reflectance spectral data obtained at various times followingapplication of acetic acid 1008. Curve 1010 in FIG. 37 is a plot of thediagnostic accuracy of the LDA model based on reflectance spectral dataobtained between about 60 and about 80 seconds (“peak whitening model”)as applied to reflectance spectra from the bins of Table 1, and curve1012 in FIG. 37 is a plot of the diagnostic accuracy of the LDA modelbased on reflectance spectral data obtained between about 160 and about180 seconds, as applied to reflectance spectra from the bins of Table 1.For the peak-whitening model, the highest accuracy was obtained at about70 s, while accuracies greater than 70% were obtained with spectracollected in a window between about 30 s and about 130 s. The 160–180 smodel had a narrower window around 70 s, but performs better at longertimes.

FIG. 38 shows the difference between the mean 337-nm fluorescencespectra for non-CIN 2/3 tissues and CIN 2/3 tissues at three times(prior to application of acetic acid (graph 1014), maximum whitening(graph 1016, about 60 to about 80 seconds post-AA), and at a timecorresponding to the latest time period in which data was obtained(graph 1018, about 160 to about 180 seconds post-AA)). The timecorresponding to maximum whitening was determined from reflectance data,and occurs between about 60 seconds and 80 seconds following applicationof acetic acid. In the absence of acetic acid, the fluorescence spectrafor CIN 2/3 tissue (curve 1020 of graph 1014 in FIG. 38) and for non-CIN2/3 tissue (curve 1022 of graph 1014 in FIG. 38) are essentiallyequivalent with a slightly lower fluorescence noted around 390 nm forCIN 2/3 sites. Following the application of acetic acid, thefluorescence of CIN 2/3 and non-CIN 2/3 tissues decrease, with CIN 2/3showing a larger relative percent change (compare curves 1024 and 1026of graph 1016 in FIG. 38). From about 160 s to about 180 s followingacetic acid application, the fluorescence of CIN 2/3 tissue shows signsof returning to the pre-acetic acid state while the fluorescence of thenon-CIN 2/3 group continues to decrease (compare curves 1028 and 1030 ofgraph 1018 in FIG. 38).

An optimal data acquisition window may also be obtained using adiscrimination function calculated from fluorescence spectra of CIN 2/3and non-CIN 2/3 tissues shown in FIG. 38. In one example, discriminationfunction spectra include values of the discrimination function inEquation 76 determined as a function of wavelength for sets of spectraldata obtained at various times. FIG. 39 shows a graph 1032 depicting thediscrimination function spectra evaluated using the fluorescence data ofFIG. 38 obtained prior to application of acetic acid, and at two timespost-AA. As shown in FIG. 39, applications of acetic acid improves thatdistinction between CIN 2/3 and non-CIN 2/3 tissues using fluorescencedata. The largest absolute values are found using data measured withinthe range of about 160–180 s post-acetic acid (curve 1042), and theseagree with the differences seen in the mean fluorescence spectra of FIG.38 (curves 1030 and 1028 of graph 1018 in FIG. 38).

Multivariate linear regression takes into account wavelengthinterdependencies in determining an optimal data acquisition window. Anapplication of one method of determining an optimal window includesclassifying data represented in the CIN 2/3, CIN 1, and NED categoriesin the Appendix Table into CIN 2/3 and non-CIN 2/3 categories by usingclassification models developed from the fluorescence data shown in FIG.38. Fluorescence intensities are down-sampled to one about every 10 nmbetween about 360 and about 720 nm. The model is trained by addingintensities in a forward manner. Testing is performed by aleave-one-spectrum-out jack-knife process. The result of this analysisshows which wavelengths best separate CIN 2/3 from non-CIN 2/3, as shownin Table 3.

TABLE 3 Forwarded selected best 337-nm fluorescence wavelengths forclassifying CIN 2/3 from non-CIN 2/3 spectra obtained at different timespre and post-AA. Time from AA (s) LDA Model Input Wavelengths (nm)Accuracy −30 380, 430, 440, 610, 660, 700, 710 61 30 370, 380, 390, 64061 50 410 54 70 360, 390, 490, 580, 590, 670 63 90 370, 380, 420, 460,500, 560, 660 64 110 360, 390, 400, 710 51 130 370 53 150 370, 380, 440,620, 640, 700 65 170 370, 480, 510, 570, 600, 700, 720 76

As shown in Table 3, the two best models for separating CIN 2/3 andnon-CIN 2/3, taking into account wavelength interdependencies, use dataobtained at peak CIN 2/3 whitening (60–80 s) and data obtained at thelatest time measured (from about 160 s to about 180 s post acetic acid).The first model uses input wavelengths between about 360 and about 670nm, while the second model uses wavelengths between about 370 and about720 nm.

FIG. 40 demonstrates one step in determining an optimal window. FIG. 40shows a graph 1044 depicting the performance of the two LDA modelsdescribed in Table 3 above as applied to fluorescence spectral dataobtained at various times following application of acetic acid 1046.Curve 1048 in FIG. 40 is a plot of the diagnostic accuracy of the LDAmodel based on fluorescence spectral data obtained between about 60 andabout 80 seconds (“peak whitening model”) as applied to fluorescencespectra from the bins of Table 1, and curve 1050 in FIG. 40 is a plot ofthe diagnostic accuracy of the LDA model based on fluorescence spectraldata obtained between about 160 and about 180 seconds, as applied tofluorescence spectra from the bins of Table 1. The accuracies of thesemodels vary depending on when the fluorescence spectra are recordedrelative to the application of acetic acid, as shown in FIG. 40. Thepredictive ability of the fluorescence models in FIG. 40 tend to be lessthan that of the reflectance models in FIG. 37. Accuracies greater than70% are obtained with spectra collected after about 160 seconds post-AA.

One embodiment includes classifying spectral data shown in FIG. 38 fromknown reference tissue samples into CIN 2/3 and non-CIN 2/3 categoriesby using classification models developed from the fluorescence data foreach of the bins in Table 1. Models are developed based on time postacetic acid. Ratios of fluorescence to reflectance are down-sampled toone every 10 nm between about 360 and about 720 nm. The model is trainedby adding intensities in a forward manner. Testing is performed by aleave-one-spectrum-out jack-knife process. For this analysis, the modelis based on intensities at about 360, 400, 420, 430, 560, 610, and 630nm. In general, the results are slightly better than a model based onfluorescence alone. Improved performance is noted from spectra acquiredat about 160 s post acetic acid.

FIG. 41 shows a graph 1052 depicting the accuracy of three LDA models asapplied to spectral data obtained at various times following applicationof acetic acid 1054, used in determining an optimal window for obtainingspectral data. Curve 1056 in FIG. 41 is a plot of the diagnosticaccuracy of the LDA model based on reflectance spectral data obtainedbetween about 60 and about 80 seconds (“peak whitening model”), alsoshown as curve 1010 in FIG. 37. Curve 1058 in FIG. 41 is a plot of thediagnostic accuracy of the LDA model based on fluorescence spectral dataobtained between about 60 and about 80 seconds (“peak whitening model”),also shown as curve 1048 in FIG. 40. Curve 1060 in FIG. 41 is a plot ofthe diagnostic accuracy of the LDA model based on fluorescence intensitydivided by reflectance, as described in the immediately precedingparagraph.

The exemplary embodiments discussed above and illustrated in FIGS. 35 to41 provide a basis for selecting an optimum window for obtainingspectral data upon application of acetic acid. Other factors to beconsidered include the time required to apply the contrast agent and toperform target focusing as shown in FIG. 27A. Another factor is the timerequired to perform a scan over a sufficient number of regions of atissue sample to provide an adequate indication of disease state withsufficient sensitivity and selectivity. Also, a consideration may bemade for the likelihood of the need for and time required for retakesdue to patient motion.

The factors and analysis discussed above indicate that an optimal dataacquisition window is a period of time from about 30 seconds followingapplication of a contrast agent (for example, a 5 volume percent aceticacid solution) to about 130 seconds following application of thecontrast agent. Other optimal windows are possible. For example, onealternative embodiment uses an optimal window with a “start” time fromabout 10 to about 60 seconds following application of acetic acid, andan “end” time from about 110 to about 180 seconds following applicationof acetic acid.

An alternative manner for determining an optimal window comprisesdetermining and using a relative amplitude change and/or rate ofamplitude change as a trigger for obtaining spectral data from a sample.By using statistical and/or heuristic methods such as those discussedherein, it is possible to relate more easily-monitored relative changesor rates-of-change of one or more optical signals from a tissue sampleto corresponding full spectrum signals that can be used incharacterizing the state of health of a given sample. For example, byperforming a discrimination function analysis, it may be found for agiven tissue type that when the relative change in reflectance at aparticular wavelength exceeds a threshold value, the correspondingfull-spectrum reflectance can be obtained and then used to accuratelyclassify the state of health of the tissue. In addition, the triggersdetermined above may be converted into optimal time windows forobtaining diagnostic optical data from a sample.

FIG. 42 shows how an optical amplitude trigger is used to determine anoptimal time window for obtaining diagnostic optical data. The graph1062 in FIG. 42 plots the normalized relative change of mean reflectancesignal 1064 from tissue samples with a given state of health as afunction of time following application of acetic acid 1066. The meanreflectance signal determined from CIN 1, CIN 2, and Metaplasia samplesare depicted in FIG. 42 by curves 1068, 1070, and 1072, respectively.FIG. 42 shows that when the normalized relative change of meanreflectance reaches or exceeds 0.75 in this example, the image intensitydata and/or the full reflectance and/or fluorescence spectrum is mostindicative of a given state of health of a sample. Thus, for CIN 2samples, for example, this corresponds to a time period between t₁ andt₂, as shown in the graph 1062 of FIG. 42. Therefore, spectral and/orimage data obtained from a tissue sample between t₁ and t₂ followingapplication of acetic acid are used in accurately determining whether ornot CIN 2 is indicated for that sample. In one embodiment, the relativechange of reflectance of a tissue sample at one or more givenwavelengths is monitored. When that relative change is greater than orequal to the 0.75 threshold, for example, more comprehensive spectraland/or image data are obtained to characterize whether the sample isindicative of CIN 2. In another embodiment, a predetermined range ofvalues of the relative optical signal change is used such that when therelative signal change falls within the predetermined range of values,additional spectral and/or image data is captured in order tocharacterize the sample.

FIG. 43 shows how a rate-of-change of an optical amplitude trigger isused to determine an optimal time window for obtaining diagnosticoptical data. The graph 1074 of FIG. 43 plots the slope of an exemplarymean reflectance signal 1076 from tissue samples with a given state ofhealth as a function of time following application of acetic acid 1078.The slope of mean reflectance is a measure of the rate of change of themean reflectance signal. The rate of change of mean reflectancedetermined from CIN 1, CIN 2, and metaplasia samples are depicted inFIG. 43 by curves 1080, 1082, and 1084, respectively. Those curves showthat when the absolute value of the slope is less than or equal to 0.1,for example, in the vicinity of maximum reflectance, the image intensitydata and/or the full reflectance and/or fluorescence spectrum is mostindicative of a given state of health of a sample. Thus, for CIN 2samples, for example, this corresponds to a time period between t₁ andt₂ as shown in the graph 1074 of FIG. 43. Therefore, spectral and/orimage data obtained from a tissue sample between t₁ and t₂ followingapplication of acetic acid is used in accurately determining whether ornot CIN 2 is indicated for that sample. In the example, the rate ofchange of reflectance of a tissue sample is monitored at one or morewavelengths. When that rate of change has an absolute value less than orequal to 0.1, more comprehensive spectral and/or image data are obtainedfrom the sample for purposes of characterizing whether or not the sampleis indicative of CIN 2. FIG. 43 demonstrates use of a range of values ofrate of optical signal change. Other embodiments use a single thresholdvalue.

Motion Tracking

In one embodiment, the tissue characterization system shown in FIG. 1comprises real-time motion tracking (step 106 in FIG. 1). Real-timetracking determines a correction for and/or compensates for amisalignment between two images of the tissue sample obtained during aspectral data scan (i.e. step 732 in FIGS. 27A and 27B), where themisalignment is caused by a shift in the position of the sample withrespect to the instrument 102 in FIG. 1 (or, more particularly, theprobe optics 178). The misalignment may be caused by unavoidable patientmotion, such as motion due to breathing during the spectral data scan732.

In one embodiment, the correction factor determined by the real-timetracker is used to automatically compensate for patient motion, forexample, by adjusting the instrument 102 (FIG. 1) so that spectral dataobtained from indexed regions of the tissue sample during the scancorrespond to their originally-indexed locations. Alternatively oradditionally, the motion correction factor can be used in spectral datapre-processing, step 114 in FIG. 1 and FIG. 11, to correct spectral dataobtained during a scan according to an applicable correction factor. Forexample, the spectral data lookup method in step 114 of FIG. 1 asdiscussed herein may compensate for patient motion by using a correctiondetermined by the real-time tracker 106 to correlate a set of spectraldata obtained during a scan with its true, motion-corrected position(x,y) on the tissue sample. In one embodiment, the motion correctionfactor determined in step 106 of FIG. 1 is updated about once everysecond during the scan using successive images of the tissue, as shownin FIG. 27B. Step 106 determines and validates a motion correctionfactor about once every second during the spectral scan, correspondingto each successive image in FIG. 27B. Then, the pre-processing component114 of FIG. 1 corrects the spectral data obtained at an interrogationpoint during the spectral scan using the correction factor correspondingto the time at which the spectral data were obtained.

A typical misalignment between two images obtained about 1 second apartis less than about 0.55-mm within a two-dimensional, 480×500 pixel imageframe field covering a tissue area of approximately 25-mm×25-mm. Thesedimensions provide an example of the relative scale of misalignmentversus image size. In some instances it is only necessary to compensatefor misalignments of less than about one millimeter within the exemplaryimage frame field defined above. In other cases, it is necessary tocompensate for misalignments of less than about 0.3-mm within theexemplary image frame field above. Also, the dimensions represented bythe image frame field, the number of pixels of the image frame field,and/or the pixel resolution may differ from the values shown above.

A misalignment correction determination may be inaccurate, for example,due to any one or a combination of the following: non-translationalsample motion such as rotational motion, local deformation, and/orwarping; changing features of a sample such as whitening of tissue; andimage recording problems such as focus adjustment, missing images,blurred or distorted images, low signal-to-noise ratio, andcomputational artifacts. Validation procedures of the invention identifysuch inaccuracies. The methods of validation may be conducted“on-the-fly” in concert with the methods of determining misalignmentcorrections in order to improve accuracy and to reduce the time requiredto conduct a given test.

In order to facilitate the automatic analysis in the tissueclassification system 100 of FIG. 1, it is often necessary to adjust formisalignments caused by tissue sample movement that occurs during thediagnostic procedure. For example, during a given procedure, in vivotissue may spatially shift within the image frame field from one imageto the next due to movement of the patient. Accurate tissuecharacterization requires that this movement be taken into account inthe automated analysis of the tissue sample. In one embodiment, spatialshift correction made throughout a spectral data scan is more accuratethan a correction made after the scan is complete, since “on-the-fly”corrections compensate for smaller shifts occurring over shorter periodsof time and since spectral data is being continuously obtainedthroughout the approximately 12 to 15 second scan in the embodiment ofFIG. 27B.

If a sample moves while a sequence of images is obtained, the proceduremay have to be repeated. For example, this may be because the shiftbetween consecutive images is too large to be accurately compensatedfor, or because a region of interest moves outside of a usable portionof the frame captured by the optical signal detection device. Stepwisemotion correction of spectral data reduces the cumulative effect ofsample movement. If correction is made only after an entire sequence isobtained, it may not be possible to accurately compensate for some typesof sample movement. On-the-fly, stepwise compensation for misalignmentreduces the need for retakes.

On-the-fly compensation may also obviate the need to obtain an entiresequence of images before making the decision to abort a failedprocedure, particularly when coupled with on-the-fly, stepwisevalidation of the misalignment correction determination. For example, ifthe validation procedure detects that a misalignment correctiondetermination is either too large for adequate compensation to be madeor is invalid, the procedure may be aborted before obtaining the entiresequence of images. It can be immediately determined whether or not theobtained data is useable. Retakes may be performed during the samepatient visit; no follow-up visit to repeat an erroneous test isrequired. A diagnostic test invalidated by excessive movement of thepatient may be aborted before obtaining the entire sequence of images,and a new scan may be completed, as long as there is enough remainingtime in the optimal time window for obtaining spectral data.

In preferred embodiments, a determination of misalignment correction isexpressed as a translational displacement in two dimensions, x and y.Here, x and y represent Cartesian coordinates indicating displacement onthe image frame field plane. In other embodiments, corrections formisalignment are expressed in terms of non-Cartesian coordinate systems,such as biradical, spherical, and cylindrical coordinate systems, amongothers. Alternatives to Cartesian-coordinate systems may be useful, forexample, where the image frame field is non-planar.

Some types of sample motion—including rotational motion, warping, andlocal deformation—may result in an invalid misalignment correctiondetermination, since it may be impossible to express certain instancesof these types of sample motion in terms of a translationaldisplacement, for example, in the two Cartesian coordinates x and y. Itis noted, however, that in some embodiments, rotational motion, warping,local deformation, and/or other kinds of non-translational motion areacceptably accounted for by a correction expressed in terms of atranslational displacement. The changing features of the tissue, as inacetowhitening, may also affect the determination of a misalignmentcorrection. Image recording problems such as focus adjustment, missingimages, blurred or distorted images, low signal-to-noise ratio (i.e.caused by glare), and computational artifacts may affect the correctiondetermination as well. Therefore, validation of a determined correctionis often required. In some embodiments, a validation step includesdetermining whether an individual correction for misalignment iserroneous, as well as determining whether to abort or continue the testin progress. Generally, validation comprises splitting at least aportion of each of a pair of images into smaller, corresponding units(subimages), determining for each of these smaller units a measure ofthe displacement that occurs within the unit between the two images, andcomparing the unit displacements to the overall displacement between thetwo images.

In certain embodiments, the method of validation takes into account thefact that features of a tissue sample may change during the capture of asequence of images. For example, the optical intensity of certainregions of tissue change during the approximately 12 to 15 seconds of ascan, due to acetowhitening of the tissue. Therefore, in one embodiment,validation of a misalignment correction determination is performed usinga pair of consecutive images. In this way, the difference between thecorresponding validation cells of the two consecutive images is lessaffected by gradual tissue whitening changes, as compared with imagesobtained further apart in time. In an alternative embodiment, validationis performed using pairs of nonconsecutive images taken within arelatively short period of time, compared with the time in which theoverall sequence of images is obtained. In other embodiments, validationcomprises the use of any two images in the sequence of images.

A determination of misalignment correction between two images isinadequate if significant portions of the images are featureless or havelow signal-to-noise ratio (i.e. are affected by glare). Similarly,validation using cells containing significant portions that arefeatureless or that have low signal-to-noise ratio may result in theerroneous invalidation of valid misalignment correction determinations.This may occur in cases where the featureless portion of the overallimage is small enough so that it does not adversely affect themisalignment correction determination. For example, analysis offeatureless validation cells may produce meaningless correlationcoefficients. One embodiment includes identifying one or morefeatureless cells and eliminating them from consideration in thevalidation of a misalignment correction determination, therebypreventing rejection of a good misalignment correction.

A determination of misalignment correction may be erroneous due to acomputational artifact of data filtering at the image borders. Forexample, in one exemplary embodiment, an image with large intensitydifferences between the upper and lower borders and/or the left andright borders of the image frame field undergoes Laplacian of Gaussianfrequency domain filtering. Since Laplacian of Gaussian frequency domainfiltering corresponds to cyclic convolution in the space-time domain,these intensity differences (discontinuities) yield a large gradientvalue at the image border, and cause the overall misalignment correctiondetermination to be erroneous, since changes between the two images dueto spatial shift are dwarfed by the edge effects. One alternativeembodiment employs pre-multiplication of image data by a Hamming windowto remove or reduce this “wraparound error.” However, one preferredembodiment employs an image-blending technique such as feathering, tosmooth any border discontinuity, while requiring only a minimal amountof additional processing time.

FIG. 44A represents a 480×500 pixel image 1086 from a sequence of imagesof in vivo human cervix tissue and shows a 256×256 pixel portion 1088 ofthe image that the motion correction step 106 in FIG. 1 uses inidentifying a misalignment correction between two images from a sequenceof images of the tissue, according to one embodiment. The image 1086 ofFIG. 44A has a pixel resolution of about 0.054-mm. The embodimentsdescribed herein show images with pixel resolutions of about 0.0547-mmto about 0.0537-mm. Other embodiments have pixel resolutions outsidethis range. In some embodiments, the images of a sequence have anaverage pixel resolution of between about 0.044-mm and about 0.064-mm.In the embodiment of FIG. 44A, step 106 of the system of FIG. 1 uses thecentral 256×256 pixels 1088 of the image 1086 for motion tracking. Analternative embodiment uses a region of different size for motiontracking, which may or may not be located in the center of the imageframe field. In the embodiment of FIG. 44A, the motion tracking step 106of FIG. 1 determines an x-displacement and a y-displacementcorresponding to the translational shift (misalignment) between the256×256 central portions 1088 of two images in the sequence of imagesobtained during a patient spectral scan.

The determination of misalignment correction may be erroneous for anynumber of various reasons, including but not limited tonon-translational sample motion (i.e. rotational motion, localdeformation, and/or warping), changing features of a sample (i.e.whitening of tissue), and image recording problems such as focusadjustment, missing images, blurred or distorted images, lowsignal-to-noise ratio, and computational artifacts. Therefore, inpreferred embodiments, validation comprises splitting an image intosmaller units (called cells), determining displacements of these cells,and comparing the cell displacements to the overall displacement. FIG.44B depicts the image represented in FIG. 44A and shows a 128×128 pixelportion 1090 of the image, made up of 16 individual 32×32 pixelvalidation cells 1092, from which data is used to validate themisalignment correction.

FIG. 45, FIGS. 46A and B, and FIGS. 47A and B depict steps inillustrative embodiment methods of determining a misalignment correctionbetween two images of a sequence, and methods of validating thatdetermination. Steps 1096 and 1098 of FIG. 45 show development of datafrom an initial image with which data from a subsequent image arecompared in order to determine a misalignment correction between thesubsequent image and the initial image. An initial image “o” ispreprocessed, then filtered to obtain a matrix of values, for example,optical luminance (brightness, intensity), representing a portion of theinitial image. In one embodiment, preprocessing comprises transformingthe three RGB color components corresponding to a given pixel into asingle luminance value. An exemplary luminance is CCIR 601, shown inEquation 63. CCIR 601 luminance may be used, for example, as a measureof the “whiteness” of a particular pixel in an image from anacetowhitening test. Different expressions for grayscale luminance maybe used, and the choice may be geared to the specific type of diagnostictest conducted. The details of step 1096 of FIG. 45 is illustrated inblocks 1130, 1132, and 1134 of FIG. 46A, where block 1130 represents theinitial color image, “o”, in the sequence, block 1132 representsconversion of color data to grayscale using Equation 63, and block 1134represents the image of block 240 after conversion to grayscale.Referring now to FIGS. 46A and 46B, FIG. 46B is a continuation of FIG.46A, linked, for example, by the circled connectors labeled A and B.Accordingly, going forward, FIGS. 46A and 46B are referred to as FIG.46.

Step 1098 of FIG. 45 represents filtering a 256×256 portion of theinitial image, for example, a portion analogous to the 256×256 centralportion 1088 of the image 1086 of FIG. 44A, using Laplacian of Gaussianfiltering. Other filtering techniques are used in other embodiments.Preferred embodiments employ Laplacian of Gaussian filtering, whichcombines the Laplacian second derivative approximation with the Gaussiansmoothing filter to reduce the high frequency noise components prior todifferentiation. This filtering step may be performed by discreteconvolution in the space domain, or by frequency domain filtering. TheLaplacian of Gaussian (LoG) filter may be expressed in terms of x and ycoordinates (centered on zero) as shown in Equation 77:

$\begin{matrix}{{{LoG}\left( {x,y} \right)} = {{- {\frac{1}{\pi\;\sigma^{4}}\left\lbrack {1 - \frac{x^{2} + y^{2}}{2\;\sigma^{2}}} \right\rbrack}}\;{\mathbb{e}}^{- \frac{x^{2} + y^{2}}{2\;\sigma^{2}}}}} & (77)\end{matrix}$where x and y are space coordinates and σ is the Gaussian standarddeviation. In one preferred embodiment, an approximation to the LoGfunction is used. Illustrative embodiments described herein include useof an approximation kernel(s) of size 9×9, 21×21, and/or 31×31. TheGaussian standard deviation, σ, is chosen in certain preferredembodiments using Equation 78:σ=LoG filter size/8.49  (78)where LoG filter size corresponds to the size of the discrete kernelapproximation to the LoG function (i.e. 9, 21, and 31 for theapproximation kernels used herein). Other embodiments employ differentkernel approximations and/or different values of Gaussian standarddeviation.

The LoG filter size may be chosen so that invalid scans are failed andvalid scans are passed with a minimum of error. Generally, use of alarger filter size is better at reducing large structured noise and ismore sensitive to larger image features and larger motion, while use ofa smaller filter size is more sensitive to smaller features and smallermotion. One embodiment of the invention comprises adjusting filter sizeto coordinate with the kind of motion being tracked and the featuresbeing imaged.

The details of step 1098 of FIG. 45 is illustrated in FIG. 46 in blocks1134, 1136, and 1138 where block 1134 represents data from the initialimage in the sequence after conversion to grayscale luminance, block1136 represents the application of the LoG filter, and block 1138represents the 256×256 matrix of data values, G_(o)(x,y), which is the“gold standard” by which other images are compared in validatingmisalignment correction determinations in this embodiment. As detailedin FIGS. 47A and 47B, one embodiment validates a misalignment correctiondetermination by comparing a given image to its preceding image in thesequence, not by comparing a given image to the initial image in thesequence as shown in FIG. 46. (Referring now to FIGS. 47A and 47B, FIG.47B is a continuation of FIG. 47A, linked, for example, by the circledconnectors labeled A, B, and C. Accordingly, going forward, FIGS. 47Aand 47B are referred to as FIG. 47.) Although FIG. 45, FIG. 46, and FIG.47 show application of the LoG filter as a discrete convolution in thespace domain, resulting in a standard expressed in space coordinates,other embodiments comprise applying the LoG filter in the frequencydomain. In either case, the LoG filter is preferably zero padded to theimage size.

The details of steps 1100 and 1102 of FIG. 45 represent preprocessing animage “i” by converting RGB values to grayscale luminance as discussedabove, and performing LoG filtering to obtain G_(i)(x,y), a matrix ofvalues from image “i” which is compared with that of another image inthe sequence in order to determine a misalignment correction between thetwo images. The details of steps 1100 and 1102 of FIG. 45 areillustrated in FIG. 46 in blocks 1140, 1142, 1144, 1146, and 1148, wheref_(i)(x,y) in block 1140 is the raw image data from image “i”, block1142 represents conversion of the f_(i)(x,y) data to gray scaleintensities as shown in block 1144, and block 1146 representsapplication of the LoG filter on the data of block 1144 to produce thedata of block 1148, G_(i)(x,y).

Similarly, steps 1106 and 1108 of FIG. 45 represent preprocessing animage “j” by converting RGB values to grayscale luminance as discussedabove, and performing LoG filtering to obtain G_(j)(x,y), a matrix ofvalues from image “j” which is compared with image “i” in order todetermine a measure of misalignment between the two images. In somepreferred embodiments, image “j” is subsequent to image “i” in thesequence. In some preferred embodiments, “i” and “j” are consecutiveimages. Steps 1106 and 1108 of FIG. 45 are illustrated in FIG. 46 inblocks 1154, 1156, 1158, 1160, and 1162, where “j” is “i+1”, the imageconsecutive to image “i” in the sequence. In FIG. 46, block 1154 is theraw “i+1” image data, block 1156 represents conversion of the “i+1” datato gray scale intensities as shown in block 1158, and block 1160represents application of the LoG filter on the data of block 1158 toproduce the data of block 1162, G_(i+1)(x,y).

Steps 1104 and 1110 of FIG. 45 represent applying a Fourier transform,for example, a Fast Fourier Transform (FFT), using G_(i)(x,y) andG_(j)(x,y), respectively, to obtain F_(i)(u,v) and F_(j)(u,v), which arematrices of values in the frequency domain corresponding to data fromimages “i” and “j”, respectively. Details of steps 1104 and 1110 of FIG.45 are illustrated in FIG. 46 by blocks 1148, 1150, 1152, 1162, 1164,and 1166, where “j” is “i+1”, the image consecutive to image “i” in thesequence. In FIG. 46, block 1148 represents the LoG filtered data,G_(i)(x,y), corresponding to image “i”, and block 1150 represents takingthe Fast Fourier Transform of G_(i)(x,y) to obtain F_(i)(u,v), shown inblock 1152. Similarly, in FIG. 46 block 1162 is the LoG filtered data,G_(i+1)(x,y), corresponding to image “i+1”, and block 1164 representstaking the Fast Fourier Transform of G_(i+1)(x,y) to obtainF_(i+1)(u,v), shown in block 1166.

Step 1112 of FIG. 45 represents computing the cross correlationF_(i)(u,v) F*_(j)(u,v), where F_(i)(u,v) is the Fourier transform ofdata from image “i”, F*_(j)(u,v) is the complex conjugate of the Fouriertransform of data from image “j”, and u and v are frequency domainvariables. The cross-correlation of two signals of length N₁ and N₂provides N₁+N₂−1 values; thus avoiding aliasing problems due tounder-sampling, the two signals should be padded with zeros up toN₁+N₂−1 samples. Details of step 1112 of FIG. 45 are represented in FIG.46 by blocks 1152, 1166, and 1168. Block 1168 of FIG. 46 representscomputing the cross correlation, F_(i)(u,v)F*_(i+1)(u,v), usingF_(i)(u,v), the Fourier transform of data from image “i”, andF*_(i+1)(u,v), the complex conjugate of the Fourier transform of datafrom image “i+1”. The cross-correlation may also be expressed as c(k,l)in Equation 79:c(k,l)=ΣΣI ₁(p,q)I ₂(p−k,q−l)  (79)where variables (k,l) can be thought of as the shifts in each of the x-and y-directions which are being tested in a variety of combinations todetermine the best measure of misalignment between two images I₁ and I₂,and where p and q are matrix element markers.

Step 1114 of FIG. 45 represents computing the inverse Fourier transformof the cross-correlation computed in step 1112. Step 1114 of FIG. 45 isrepresented in FIG. 46 by block 1170. The resulting inverse Fouriertransform maps how well the 256×256 portions of images “i” and “j” matchup with each other given various combinations of x- and y-shifts.Generally, the normalized correlation coefficient closest to 1.0corresponds to the x-shift and y-shift position providing the bestmatch, and is determined from the resulting inverse Fourier transform.In a preferred embodiment, correlation coefficients are normalized bydividing matrix values by a scalar computed as the product of the squareroot of the (0,0) value of the auto-correlation of each image. In thisway, variations in overall brightness between the two images have a morelimited effect on the correlation coefficient, so that the actualmovement within the image frame field between the two images is betterreflected in the misalignment determination.

Step 1116 of FIG. 45 represents determining misalignment values d_(x),d_(y), d, sum(d_(x)), sum(d_(y)), and Sum(d_(j)), where d_(x) is thecomputed displacement between the two images “i” and “j” in thex-direction, d_(y) is the computed displacement between the two imagesin the y-direction, d is the square root of the sum d_(x) ²+d_(y) ² andrepresents an overall displacement between the two images, sum(d_(x)) isthe cumulative x-displacement between the current image “j” and thefirst image in the sequence “o”, sum(d_(y)) is the cumulativey-displacement between the current image “j” and the first image in thesequence “o”, and Sum(d_(j)) is the cumulative displacement, d, betweenthe current image “j” and the first image in the sequence “o”. Step 1116of FIG. 45 is represented in FIG. 46 by blocks 1172, 1174, and 1176.Blocks 1174 and 1176 represent finding the maximum value in the data ofblock 1172 in order to calculate d_(x), d_(y), d, sum(d_(x)),sum(d_(y)), and Sum(d_(i+1)) as described above, where image “j” in FIG.45 is “i+1” in FIG. 46, the image consecutive to image “i”. For example,in the scan illustrated by block 732 in FIG. 27B, if image “i” is theimage at block 750, then image “j” is the next consecutive image (theimage at block 752).

Steps 1118, 1120, and 1122 of FIG. 45 represent one method of validatingthe misalignment correction determined for image “j” in step 1116 ofFIG. 45. This method of validating misalignment correction isrepresented in blocks 1177, 1179, 1181, 1190, 1192, and 1194 of FIG. 47.Another method of validating a misalignment correction is represented insteps 1124, 1126, and 1128 of FIG. 45; and this method is represented inblocks 1178, 1180, 1182, 1184, 1186, and 1188 of FIG. 46. FIG. 47 is aschematic flow diagram depicting steps in a version of the methods shownin FIG. 45 of determining a correction for a misalignment between twoimages in which validation is performed using data from two consecutiveimages. One embodiment includes using consecutive or near-consecutiveimages to validate a misalignment correction determination, as in FIG.47. Other embodiments comprise using the initial image to validate amisalignment correction determination for a given image, as in FIG. 46.

In FIG. 45, step 1118 represents realigning G_(j)(x,y), the LoG-filtereddata from image “j”, to match up with G_(i)(x,y), the LoG-filtered datafrom image “i”, using the misalignment values d_(x) and d_(y) determinedin step 1116. In preferred embodiments, image “j” is consecutive toimage “i” in the sequence of images. Here, image “j” is image “i+1” suchthat G_(i)(x,y) is aligned with G_(i+1)(x,y) as shown in block 1177 ofFIG. 47. Similarly, in FIG. 45, step 1124 represents realigningG_(j)(x,y), the LoG-filtered data from image “j”, to match up withG_(o)(x,y), the LoG-filtered “gold standard” data from the initial image“o”, using the displacement values sum(d_(x)) and sum(d_(y)) determinedin step 1116. Step 1124 of FIG. 45 is represented in block 1178 of FIG.46.

Step 1120 of FIG. 45 represents comparing corresponding validation cellsfrom G_(j)(x y) and G_(i)(x,y) by computing correlation coefficients foreach cell. This is represented schematically in FIG. 47 by blocks 1179,1181, 1190, 1192, and 1194 for the case where j=i+1. First, a 128×128pixel central portion of the realigned G_(i+1)(x,y) is selected, and thecorresponding 128×128 pixel central portion of G_(i)(x,y) is selected,as shown in blocks 1179 and 1181 of FIG. 47. An exemplary 128×128 pixelvalidation region 1090 is shown in FIG. 44B. Then, one embodimentcomprises computing a correlation coefficient for each of 16 validationcells. An exemplary validation cell from each of the realignedG_(i+1)(x,y) matrix 1181 and G_(i)(x,y) matrix 1179 is shown in blocks1192 and 1190 of FIG. 47. The validation cells are as depicted in the32×32 pixel divisions 1092 of the 128×128 pixel validation region 1090of FIG. 44B. Different embodiments use different numbers and/ordifferent sizes of validation cells. Correlation coefficients arecomputed for each of the 16 cells, as shown in block 1194 of FIG. 47.Each correlation coefficient is a normalized cross-correlationcoefficient as shown in Equation 80:

$\begin{matrix}{{c^{\prime}\left( {m,n} \right)} = \frac{\sum{\sum{{I_{1}\left\lbrack {p,q} \right\rbrack} \times {I_{2}\left\lbrack {p,q} \right\rbrack}}}}{\sqrt{\sum{\sum{I_{1}^{2}\left\lbrack {p,q} \right\rbrack}}}\sqrt{\sum{\sum{I_{2}^{2}\left\lbrack {p,q} \right\rbrack}}}}} & (80)\end{matrix}$where c′(m,n) is the normalized cross-correlation coefficient for thevalidation cell (m,n), m is an integer 1 to 4 corresponding to thecolumn of the validation cell whose correlation coefficient is beingcalculated, n is an integer 1 to 4 corresponding to the row of thevalidation cell whose correlation coefficient is being calculated, p andq are matrix element markers, I₁[p,q] are elements of the cell in columnm and row n of the 128×128 portion of the realigned image shown in block1181 of FIG. 47, and 12[p,q] are elements of the cell in column m androw n of the 128×128 portion of G_(i)(x,y) shown in block 1179 of FIG.47. In that figure, p=1 to 32 and q=1 to 32, and the sums shown inEquation 80 are performed over p and q. The cross-correlationcoefficient of Equation 80 is similar to an auto-correlation in thesense that a subsequent image is realigned with a prior image based onthe determined misalignment correction so that, ideally, the alignedimages appear to be identical. A low value of c′(m,n) indicates amismatching between two corresponding cells. The misalignment correctiondetermination is then either validated or rejected based on the valuesof the 16 correlation coefficients computed in step 1194 of FIG. 47. Forexample, each correlation coefficient may be compared against athreshold maximum value. This corresponds to step 1122 of FIG. 45.

Step 1126 of FIG. 45 represents comparing corresponding validation cellsfrom G_(j)(x,y) and G_(o)(x,y) by computing correlation coefficients foreach cell. This is represented schematically in FIG. 46 by blocks 1180,1182, 1184, 1186, and 1188 for the case where j=i+1. First, a 128×128pixel central portion of the realigned G_(i+1)(x,y) is selected, and thecorresponding 128×128 pixel central portion of G_(o)(x,y) is selected,as shown in blocks 1182 and 1180 of FIG. 46. An exemplary 128×128 pixelvalidation region 1090 is shown in FIG. 44B. Then, one embodimentcomprises computing a correlation coefficient for each of the 16validation cells. An exemplary validation cell from each of therealigned G_(i+1)(x,y) matrix 1182 and G_(o)(x,y) matrix 1180 is shownin blocks 1186 and 1184 of FIG. 46. The validation cells are as depictedin the 32×32 pixel divisions 1092 of the 128×128 pixel validation region1090 of FIG. 44B. Other embodiments use different numbers of and/ordifferent sizes of validation cells. Correlation coefficients arecomputed for each of the 16 cells, as shown in block 1188 of FIG. 46.Each correlation coefficient is a normalized “auto”-correlationcoefficient as shown in Equation 80 above, where I₁[p,q] are elements ofthe cell in column m and row n of the 128×128 portion of the realignedsubsequent image shown in block 1182 of FIG. 46, and I₂[p,q] areelements of the cell in column m and row n of the 128×128 portion ofG_(o)(x,y) shown in block 1180 of FIG. 46. A low value of c′(m,n)indicates a mismatching between two corresponding cells. Themisalignment determination is then either validated or rejected based onthe values of the 16 correlation coefficients computed in step 1188 ofFIG. 46. This corresponds to step 1128 of FIG. 45.

In one embodiment, determinations of misalignment correction andvalidation of these determinations as shown in each of FIG. 45, FIG. 46,and FIG. 47 are performed using a plurality of the images in sequence.In one embodiment, determinations of misalignment correction andvalidations thereof are performed while images are being obtained, sothat an examination in which a given sequence of images is obtained maybe aborted before all the images are obtained. In some embodiments, amisalignment correction is determined, validated, and compensated for byadjusting the optical signal detection device obtaining the images. Incertain embodiments, an adjustment of the optical signal detectiondevice is made after each of a plurality of images are obtained. Incertain embodiments, an adjustment, if required by the misalignmentcorrection determination, is made after every image subsequent to thefirst image (except the last image), and prior to the next consecutiveimage. In one embodiment, a cervical tissue scan comprising a sequenceof 13 images is performed using on-the-fly misalignment correctiondetermination, validation, and camera adjustment, such that the scan iscompleted in about 12 seconds. Other embodiments comprise obtainingsequences of any number of images in more or less time than indicatedhere.

Each of steps 1122 and 1128 of the embodiment of FIG. 45 representsapplying a validation algorithm to determine at least the following: (1)whether the misalignment correction can be made, for example, byadjusting the optical signal detection device, and (2) whether themisalignment correction determined is valid. In an exemplary embodiment,the validation algorithm determines that a misalignment correctioncannot be executed during an acetowhitening exam conducted on cervicaltissue in time to provide sufficiently aligned subsequent images, ifeither of conditions (a) or (b) is met, as follows: (a) d_(i), thedisplacement between the current image “i” and the immediately precedingimage “i−1” is greater than 0.55-mm or (b) Sum(d_(i)), the totaldisplacement between the current image and the first image in thesequence, “o”, is greater than 2.5-mm. If either of these conditions ismet, the spectral scan in progress is aborted, and another scan must beperformed. If sufficient time remains within the optimal time window forobtaining spectral data, a fresh scan may begin immediately after aprevious scan is aborted. Other embodiments may comprise the use ofdifferent validation rules. In one embodiment, if only condition (a) ismet, the system retakes image “i” while continuing the spectral scan,and if condition (b) is met, the spectral scan is aborted and must berestarted if sufficient time remains within the optimal window.

In one embodiment, validation is performed for each determination ofmisalignment correction by counting how many of the correlationcoefficients c′_(r)(m,n) shown in Equation 80 (corresponding to the 16validation cells) is less than 0.5. If this number is greater than 1,the scan in progress is aborted. In one embodiment, if there are morethan three correlation coefficients c′_(r)(m,n) less than 0.35, then thescan is aborted. Other embodiments comprise the use of differentvalidation rules. Gradual changes in image features, such asacetowhitening of tissue or changes in glare, cause discrepancies whichare reflected in the correlation coefficients of the validation cells,but which do not represent a spatial shift. Thus, in preferredembodiments, the validation is performed as shown in FIG. 47, wherevalidation cells of consecutive images are used to calculate thecorrelation coefficients. In other embodiments, the validation isperformed as shown in FIG. 46, where validation cells of a currentimage, “i”, and an initial image of the sequence, “o”, are used tocalculate the correlation coefficients of Equation 80.

FIGS. 48A–F depict a subset of adjusted, filtered images 1200, 1204,1208, 1212, 1216, and 1220 from a sequence of images of a tissue with anoverlay of gridlines showing the validation cells used in validating thedeterminations of misalignment correction between the images, accordingto an illustrative embodiment of the invention. By performing validationaccording to FIG. 47, using consecutive images to calculate thecorrelation coefficients of Equation 80, the number of validation cellswith correlation coefficient below 0.5 for the misalignment-correctedimages of FIGS. 48A–F is 0, 1, 0, 0, and 1 for images 1204, 1208, 1212,1216, and 1220, respectively. Since none of the images have more thanone coefficient below 0.5, this sequence is successful and is notaborted. There is only a gradually changing glare, seen to move withinthe validation region 1202, 1206, 1210, 1214, 1218, 1222 of each image.In an embodiment in which validation is performed as in FIG. 46, thenumber of validation cells with correlation coefficient below 0.5 forthe misalignment-corrected images of FIGS. 48A–F is 3, 4, 5, 5, and 6for images 1204, 1208, 1212, 1216, and 1220, respectively. This is not agood result in this example, since the exam would be erroneouslyaborted, due only to gradual changes in glare or whitening of tissue,not uncompensated movement of the tissue sample.

Alternatively, validation cells that are featureless or have lowsignal-to-noise ratio are eliminated from consideration. Those cells canproduce meaningless correlation coefficients. Featureless cells in apreferred embodiment are identified and eliminated from consideration byexamining the deviation of the sum squared gradient of a givenvalidation cell from the mean of the sum squared gradient of all cellsas shown in Equation 81:IF ssg ₁(m,n)<Mean[ssg(m,n)]−STD[ssg(m,n)], THEN set c′₁(m,n)=1.0.  (81)where c′₁(m,n) is the correlation of the given validation cell “1”,ssg₁(m,n)=ΣΣI₁ ²[p,q], m=1 to 4, n=1 to 4, I₁[p,q] is the matrix ofvalues of the given validation cell “1”, p=1 to 32, q=1 to 32, thesummations ΣΣ are performed over pixel markers p and q, Mean[ssg(m,n)]is the mean of the sum squared gradient of all 16 validation cells, andSTD[ssg(m,n)] is the standard deviation of the sum squared gradient ofthe given validation cell “1” from the mean sum squared gradient. Bysetting c′₁(m,n)=1.0 for the given validation cell, the cell does notcount against validation of the misalignment correction determination inthe rubrics of either step 1122 or step 1128 of FIG. 45, since acorrelation coefficient of 1.0 represents a perfect match.

If an image has large intensity differences between the upper and lowerborders and/or the left and right borders of the image frame field, LoGfiltering may result in “wraparound error.” A preferred embodimentemploys an image blending technique such as “feathering” to smoothborder discontinuities, while requiring only a minimal amount ofadditional processing time.

FIG. 49A depicts a sample image 1224 after application of a 9-pixel size[9×9] Laplacian of Gaussian filter (LoG 9 filter) on an exemplary imagefrom a sequence of images of tissue, according to an illustrativeembodiment of the invention. The filtered intensity values are erroneousat the top edge 1226, the bottom edge 1228, the right edge 1232, and theleft edge 1230 of the image 1224. Since LoG frequency domain filteringcorresponds to cyclic convolution in the space-time domain, intensitydiscontinuities between the top and bottom edges of an image and betweenthe right and left edges of an image result in erroneous gradientapproximations. These erroneous gradient approximations can be seen inthe dark stripe on the right edge 1232 and bottom edge 1228 of the image1224, as well as the light stripe on the top edge 1226 and the left edge1230 of the image 1224. This often results in a misalignment correctiondetermination that is too small, since changes between the images due tospatial shift are dwarfed by the edge effects. A preferred embodimentuses a “feathering” technique to smooth border discontinuities andreduce “wraparound error.”

Feathering comprises removal of border discontinuities prior toapplication of a filter. In preferred embodiments, feathering isperformed on an image before LoG filtering, for example, between steps1100 and 1102 in FIG. 45. In embodiments where LoG filtering isperformed in the frequency domain (subsequent to Fouriertransformation), feathering is preferably performed prior to bothFourier transformation and LoG filtering. For two-dimensional imageintensity (luminance) functions I₁(x,y) and I₂(x,y) that arediscontinuous at x=x₀, an illustrative feathering algorithm is asfollows:

$\begin{matrix}\begin{matrix}{{I_{1}^{\prime}\left( {x,y} \right)} = {{{I_{1}\left( {x,y} \right)} \cdot {f\left( {\frac{x - x_{0}}{d} + 0.5} \right)}}\mspace{14mu}{and}}} \\{{{I_{2}^{\prime}\left( {x,y} \right)} = {{I_{2}\left( {x,y} \right)} \cdot \left( {1 - {f\left( {\frac{x - x_{0}}{d} + 0.5} \right)}} \right)}},} \\{{f(x)} = \left\{ {\begin{matrix}0 & {x < 0} \\{{3x^{2}} - {2x^{3}}} & {0 \leq x \leq 1} \\0 & {x > 1}\end{matrix},} \right.}\end{matrix} & (82)\end{matrix}$where I₁′(x,y) and I₂′(x,y) are the intensity (luminance) functionsI₁(x,y) and I₂(x,y) after applying the feathering algorithm of Equation82, and d is the feathering distance chosen. The feathering distance, d,adjusts the tradeoff between removing wraparound error and suppressingimage content.

FIG. 49B depicts the application of both a feathering technique and aLoG filter on the same unfiltered image used in FIG. 49A. The featheringis performed to account for border processing effects, according to anillustrative embodiment of the invention. Here, a feathering distance,d, of 20 pixels was used. Other embodiments use other values of d. Thefiltered image 1234 of FIG. 49B does not display uncharacteristicallylarge or small gradient intensity values at the top edge 1236, bottomedge 1238, right edge 1242, or left edge 1240, since discontinuities aresmoothed prior to LoG filtering. Also, there is minimal contrastsuppression of image detail at the borders. Pixels outside thefeathering distance, d, are not affected. The use of feathering hereresults in more accurate determinations of misalignment correctionbetween two images in a sequence of images.

Another method of border smoothing is multiplication of unfiltered imagedata by a Hamming window. In some embodiments, a Hamming window functionis multiplied to image data before Fourier transformation so that theborder pixels are gradually modified to remove discontinuities. However,application of the Hamming window suppresses image intensity as well asgradient information near the border of an image.

FIG. 50A is identical to FIG. 49A and depicts the application of a LoG 9filter on an exemplary image from a sequence of images of tissueaccording to an illustrative embodiment of the invention. The filteredintensity values are erroneous at the top edge 1226, the bottom edge1228, the right edge 1232, and the left edge 1230 of the image 1224.

FIG. 50B depicts the application of both a Hamming window and a LoG 9filter on the same unfiltered image used in FIG. 50A. Hamming windowingis performed to account for border processing effects, according to anillustrative embodiment of the invention. Each of the edges 1246, 1248,1250, 1252 of the image 1244 of FIG. 50B no longer show the extremefiltered intensity values seen at the edges 1226, 1228, 1230, 1232 ofthe image 1224 of FIG. 50A. However, there is a greater suppression ofimage detail in FIG. 50B than in FIG. 49B. Thus, for this particularembodiment, application of the feathering technique is preferred overapplication of Hamming windowing.

One embodiment includes removing cyclic convolution artifacts by zeropadding the image prior to frequency domain filtering to assure imagedata at an edge would not affect filtering output at the opposite edge.This technique adds computational complexity and may increase processingtime.

FIGS. 51A–F depict the determination of a misalignment correctionbetween two images using methods including the application of LoGfilters of various sizes, as well as the application of a Hamming windowtechnique and a feathering technique, according to illustrativeembodiments of the invention. Image 1254 and image 1256 of FIGS. 51A–Bare consecutive images from a sequence of images of cervix tissueobtained during a diagnostic exam, each with a pixel resolution of about0.054-mm. FIGS. 51C–F depict the application of four different imagefiltering algorithms: (1) Hamming window with LoG 9 filtering, (2)feathering with LoG 9 filtering, (3) feathering with LoG 21 filtering,and (4) feathering with LoG 31 filtering. Each of these algorithms areimplemented as part of a misalignment correction determination andvalidation technique as illustrated in FIG. 45 and FIG. 47, and valuesof d_(x) and d_(y) between images 1254 and 1256 of FIGS. 51A–B aredetermined using each of the four filtering algorithms. For image 1254,each of the four different image filtering algorithms (1)–(4) listedabove are applied, resulting in images 1258, 1262, 1266, and 1270,respectively, each having 256×256 pixels. The four different imagefiltering algorithms are also applied for image 1256, resulting inimages 1260, 1264, 1268, and 1272, respectively, each having 256×256pixels. Values of (d_(x), d_(y)) determined using Hamming+LoG 9filtering are (−7, 0), expressed in pixels. Values of (d_(x), d_(y))determined using feathering+LoG 9 filtering are (−2, −10). Values of(d_(x), d_(y)) determined using feathering+LoG 21 filtering are (−1,−9). Values of (d_(x), d_(y)) determined using feathering+LoG 31filtering are (0, −8). All of the displacement values determined usingfeathering are close in this embodiment, and agree well withvisually-verified displacement. However, in this example, thedisplacement values determined using Hamming windowing are differentfrom those obtained using the other three filtering methods, and resultin a misalignment correction that does not agree well withvisually-verified displacement. Thus, for this example, feathering worksbest since it does not suppress as much useful image data.

The effect of the filtering algorithm employed, as well as the choice ofvalidation rules are examined by applying combinations of the variousfiltering algorithms and validation rules to pairs of sequential imagesof tissue and determining the number of “true positives” and “falsepositives” identified. A true positive occurs when a bad misalignmentcorrection determination is properly rejected by a given validationrule. A false positive occurs when a good misalignment correctiondetermination is improperly rejected as a failure by a given validationrule. The classification of a validation result as a “true positive” ora “false positive” is made by visual inspection of the pair ofsequential images. In preferred embodiments, whenever true failuresoccur, the scan should be aborted. Some examples of situations wheretrue failures occur in certain embodiments include image pairs betweenwhich there is one or more of the following: a large non-translationaldeformation such as warping or tilting; a large jump for which motiontracking cannot compute a correct translational displacement; rotationgreater than about 3 degrees; situations in which a target laser is lefton; video system failure such as blur, dark scan lines, or frameshifting; cases where the image is too dark and noisy, in shadow; caseswhere a vaginal speculum (or other obstruction) blocks about half theimage; other obstructions such as sudden bleeding.

In one embodiment, a set of validation rules is chosen such that truepositives are maximized and false positives are minimized. Sensitivityand specificity can be adjusted by adjusting choice of filteringalgorithms and/or choice of validation rules. Table 4 shows the numberof true positives (true failures) and false positives (false failures)determined by a validation rule as depicted in FIG. 45 and FIG. 47 wherevalidation is determined using consecutive images. Table 4 shows variouscombinations of filtering algorithms and validation rules. The fourfiltering algorithms used are (1) Hamming windowing with LoG 9filtering, (2) feathering with LoG 9 filtering, (3) feathering with LoG21 filtering, and (4) feathering with LoG 31 filtering. The values,c′(m,n), correspond to the normalized “auto”-correlation coefficient ofEquation 80 whose value must be met or exceeded in order for avalidation cell to “pass” in an embodiment. The “Number Threshold”column indicates the maximum number of “failed” validation cells, out ofthe 16 total cells, that are allowed for a misalignment correctiondetermination to be accepted in an embodiment. If more than this numberof validation cells fail, then the misalignment correction determinationis rejected.

TABLE 4 True positives and false positives of validation determinationsfor embodiments using various combinations of filtering algorithms andvalidation rules. Number c′(m, n) Threshold TP FP Hamming LoG 9 −0.1 134 28 Feathering LoG 9 −0.1 3 19 17 Feathering LoG 21 0.3 2 46 10 0.35 352 4 Feathering LoG 31 0.5 3 48 3

For the given set of cervical image pairs on which the methods shown inTable 4 were applied, feathering performs better than Hamming windowing,since there are more true positives and fewer false positives. Amongdifferent LoG filter sizes, LoG 21 and LoG 31 performs better than LoG 9for both tracking and validation here. The LoG 21 filter is moresensitive to rotation and deformation than the LoG 31 filter for theseexamples. One embodiment of the determination and validation ofmisalignment corrections between 256×256 pixel portions of images ofcervical tissue with pixel resolution of about 0.054-mm employs one ormore of the following: (1) use of feathering for image borderprocessing, (2) application of LoG 21 filter, (3) elimination ofvalidation cells with low signal-to-noise ratio, and (4) use ofconsecutive images for validation.

Broadband Reflectance Arbitration and Low-Signal Masking

A tissue characterization system as shown in FIG. 1 also may comprisearbitrating between two or more redundant sets of spectral data asdepicted in step 128 of FIG. 1. In one embodiment shown in FIG. 1, step128 includes arbitrating between two sets of broadband reflectance dataobtained in step 104 during a spectral scan for each interrogation pointof a tissue sample. Data are obtained at each interrogation point usinglight incident to the interrogation point at two different angles, asdepicted in FIG. 8. In this way, if only one set of reflectance data isaffected by an artifact such as glare or shadow, the other set can beused in tissue classification, for example, in step 132 of FIG. 1. Thearbitration step 128 in FIG. 1 determines whether either of the two setsof reflectance spectral data at each point is affected by an artifact.Step 128 also determines a single set of reflectance data from eachinterrogation point to be used in tissue classification if at least oneof the two sets is acceptably unaffected by an artifact. As used here,artifacts identified in the arbitration step 128 of FIG. 1 include, forexample, both lighting artifacts and obstruction artifacts—such asglare, shadow, blood, mucus, a speculum, smoke tube tissue, and/or ostissue.

In the embodiment shown in FIG. 1, step 128 additionally includes afirst-level “hard masking” of certain interrogation points. For example,interrogation points are considered “indeterminate” where values of bothsets of reflectance spectral data and/or values of the set offluorescence data are low due to shadow or an obstruction. Additionalspectral masks, both hard masks and soft masks, are determined in oneembodiment in step 130 of FIG. 1. As discussed herein, hard-masking ofdata includes eliminating identified, potentially non-representativedata from further consideration and identifying the corresponding tissueregion as “indeterminate”, while soft-masking includes applying aweighting function or weighting factor to identified, potentiallynon-representative data so that the importance of the data as adiagnostic indicator of a tissue region in a tissue classificationalgorithm is thereby reduced. A point that is soft-masked is notnecessarily identified as “indeterminate”.

The diagram 284 of FIG. 8 shows that a misalignment of the probe 142 maycreate conditions where either or both of the top and bottom speculumblades 286 block part or all of the illumination path from either orboth of the intersecting upper and lower cones of illuminating light196, 198, thereby affecting the spectral data obtained for the region250 of the tissue sample 194. The speculum blades, or other obstructionspresent during a spectral scan, may physically obstruct the region 250being analyzed, or may partially obstruct the light illuminating theregion 250 causing a shadow. In either case, the spectral data obtainedmay be adversely affected and rendered unusable for characterizing theregion of the tissue sample. Obtaining multiple sets of spectral datausing illumination from sources at various positions and angles improvesthe chances of obtaining at least one set of spectral data that is notaffected by glare, shadow, and/or obstructions.

FIG. 52 shows a graph 1276 depicting exemplary mean values ofreflectance spectral data 1278 as a function of wavelength 1280 fortissue regions affected by glare 1282, tissue regions affected by shadow1284, and tissue regions affected by neither glare nor shadow 1286according to an illustrative embodiment of the invention. Thereflectance spectral data 1278 represent the fraction of incident lightthat is reflected from the sample. The graph 1276 shows that thereflectance values of a region of tissue affected by glare 1282 arehigher at all measured wavelengths than the reflectance of a region oftissue not affected by glare 1286. The graph 1276 also shows that thereflectance values of a region of tissue with illumination partiallyblocked by a speculum blade such that the region is in shadow 1284, arelower at all measured wavelengths than the reflectance of a region oftissue not affected by shadow 1286. The shapes of all three curves 1282,1284, 1286 are different. In this example, the data affected by glare orshadow may not be usable to determine a condition or characteristic ofthe region of the sample, if the data are not representative of theregion of the tissue sample. Hence, glare and shadow may adverselyaffect spectral data obtained for a region of a tissue sample.

In one embodiment, step 104 of FIG. 1 comprises obtaining onefluorescence spectrum and two broadband reflectance spectra at each of aplurality of scan locations of the sample tissue (interrogation points).Here, a spectrum refers to a collection of spectral data over a range ofwavelengths. In one embodiment method, spectral data are collected overa range of wavelengths between 360 and 720 nm in 1 nm increments. Inother embodiments, the range of wavelengths lies anywhere between about190 nm and 1100 nm. Here, the two reflectance spectra are referred to asthe BB1 (broadband one) and BB2 (broadband two) spectra. BB1 and BB2differ in the way that the tissue is illuminated at the time thespectral data are obtained as described below. In the embodiment shownin FIG. 6, the probe head 192 has 4 illumination sources 222, 224, 226,228 located circumferentially about the collection optics 200. Twosources are above 222, 224 and two are below the horizontal plane 226,228, as illustrated in the second arrangement 212 of FIG. 6. The twoupper sources are used to obtain BB1 spectra and the two lower sourcesare used to obtain BB2 spectra. Since the upper and lower sourcesilluminate a region of the tissue sample using light incident to theregion at different angles, an artifact—for example, or shadow—mayaffect one of the two reflectance spectra obtained for the region, whilethe other reflectance spectrum is unaffected. For example, duringacquisition of spectral data, the BB1 spectrum may be unaffected by anartifact even if the BB2 spectrum is adversely affected by the artifact.In such a case, BB1 spectral data may be used to characterize thecondition of the region of tissue, for example, in step 132 of FIG. 1,even though the BB2 data is not representative of the region. In otherembodiments, the BB1 and BB2 spectra comprise one or more other types ofspectral data, such as absorbance spectra, adsorption spectra,transmission spectra, fluorescence spectra, and/or other types ofoptical and atomic emission spectra.

FIG. 53 shows a graph 1287 depicting mean values and standard deviationsof broadband reflectance spectral data using the BB1 channel lightsource for regions confirmed as being obscured by blood, obscured bymucus, obscured by glare from the BB1 source, obscured by glare from theBB2 source, or unobscured, according to an illustrative embodiment ofthe invention. Various sample test points corresponding to regions oftissue from patient scans were visually identified as having blood,mucus, or glare present. A sample point was identified as having bloodpresent if it was completely covered by blood and if there was no glare.A sample point was identified as having mucus present if it wascompletely covered by mucus and if there was no glare. A sample pointwas identified as having glare based on visual evidence of glare andlarge reflectance values in at least one of the two sets of reflectancespectral data (the BB1 spectrum or the BB2 spectrum). FIG. 53 shows therange of BB1 reflectance values 1288 for a given category of the sampletest points which lie within one standard deviation of the mean for thecategory, plotted as a function of wavelength 1290. FIG. 53 shows rangesof BB1 reflectance values 1288 for each of the following categories ofsample test points: those identified as having blood present 1292, thoseidentified as having mucus present 1294, those identified as havingglare from the BB1 illumination source 1296, those identified as havingglare from the BB2 illumination source 1298, and those identified asunobstructed tissue 1300.

Similarly, FIG. 54 shows a graph 1301 depicting mean values and standarddeviations of broadband reflectance spectral data using the BB2 channellight source for regions confirmed as being obscured by blood 1304,obscured by mucus 1306, obscured by glare from the BB1 source 1308,obscured by glare from the BB2 source 1310, or unobscured 1312,according to an illustrative embodiment of the invention. FIG. 54 showsthe range of BB2 reflectance values 1302 for a given category of thesample test points which lie within one standard deviation of the meanfor the category, plotted as a function of wavelength 1290. FIG. 54shows ranges of BB2 reflectance values 1302 for each of the followingcategories of sample test points: those identified as having bloodpresent 1304, those identified as having mucus present 1306, thoseidentified as having glare from the BB1 illumination source 1308, thoseidentified as having glare from the BB2 illumination source 1310, andthose identified as unobstructed tissue 1312.

FIGS. 53 and 54 show that a region with glare from one illuminationsource does not necessarily have high reflectance values correspondingto data obtained using the other illumination source. For example, inFIG. 53, the range of BB1 reflectance values 1288 of points with visualevidence of glare from the BB2 source 1298 is similar to the range ofBB1 reflectance values 1288 of unobstructed tissue 1300. Similarly, inFIG. 54, the range of BB2 reflectance values 1302 of pointsdemonstrating glare from the BB1 source 1308 is similar to the range ofBB2 reflectance values 1302 of unobstructed tissue 1312. Therefore, oneof the two sets of reflectance spectral data may be useful incharacterizing the tissue even if the other of the two sets is corruptedby an artifact, such as glare.

It may also be desirable to determine spectral characteristics caused byvarious artifacts so that data corresponding to a region affected by agiven artifact may be identified or to determine a spectralcharacteristic of an artifact based on the spectral data itself, withouthaving to rely on other visual evidence of a given artifact. In order todetermine these spectral characteristics, an embodiment of the inventioncomprises using spectral data known to be affected by a given artifactbased on visual evidence, as well as spectral data known not to beaffected by an artifact. Techniques that may be used to identifyspectral characteristics and/or to develop classification rulesdetermining whether given data are affected by an artifact include, forexample, discriminant analysis (linear, nonlinear, multivariate), neuralnetworks, principal component analysis, and decision tree analysis. Oneembodiment comprises determining a particular wavelength that gives thegreatest difference between the artifact-affected spectral data (theoutlier) and spectral data from corresponding nearby tissue that isknown to be unaffected by the artifact (the tissue). Alternatively, theembodiment comprises determining a wavelength that gives the largestdifference between the outlier and the tissue, as weighted by a measureof variability of the data. In one embodiment, this method locates wherethe difference between the mean reflectance for the outlier and thetissue is at a maximum relative to the difference between the standarddeviations for the outlier data and the tissue data. In one embodiment,the method determines a maximum value of D as a function of wavelength,where D is the difference given in Equation 83 below:

$\begin{matrix}{{{D(\lambda)} = \frac{{{\mu\left( {{BB}(\lambda)} \right)}_{Outlier} - {\mu\left( {{BB}(\lambda)} \right)}_{Tissue}}}{\sqrt{{\sigma^{2}\left( {{BB}(\lambda)} \right)}_{Outlier} + {\sigma^{2}\left( {{BB}(\lambda)} \right)}_{Tissue}}}},} & (83)\end{matrix}$where μ(BB(λ))_(Outlier) is the mean of a set of reflectance spectraldata at wavelength λ known to be affected by a given artifact,μ(BB(λ))_(Tissue) is the mean of a set of reflectance spectral data atwavelength λ that is known not to be affected by the artifact,σ(BB(λ))_(Outlier) is the standard deviation of the set of reflectancespectral data at wavelength λ known to be affected by the givenartifact, and σ(BB(λ))_(Tissue) is the standard deviation of the set ofreflectance spectral data at wavelength λ known not to be affected bythe given artifact.

FIG. 55 shows a graph 1313 depicting the weighted difference 1314between the mean reflectance values of glare-obscured regions andunobscured regions of tissue as a function of wavelength 1316, accordingto an illustrative embodiment of the invention. The weighted difference1314 is as given in Equation 83. For the data sets used in FIG. 55, thewavelength providing the maximum value 1318 of D in Equation 83 is about420 nm. Thus, exemplary spectral characteristics identifiable with thisset of glare-obscured “outlier” data include the reflectance spectraldata at around 420 nm, and any deviation of this data from reflectancespectral “tissue” data for unobscured regions of correspondingly similartissue at around 420 nm. This embodiment uses reflectance spectral data.Other embodiments may use other types of spectral data, includingfluorescence data.

FIG. 56 shows a graph 1319 depicting the weighted difference 1314between the mean reflectance values of blood-obscured regions andunobscured regions of tissue as a function of wavelength 1316, accordingto an illustrative embodiment of the invention. The weighted differenceis as given in Equation 83. For the data sets used in FIG. 56, thewavelength providing the maximum value 1320 of D in Equation 83 is about585 nm.

Thus, exemplary spectral characteristics identifiable with this set ofblood-obscured “outlier” data include the reflectance spectral data atabout 585 nm, and any deviation of this data from reflectance spectral“tissue” data for unobscured regions of correspondingly similar tissueat about 585 nm. This embodiment uses reflectance spectral data. Otherembodiments may use other types of spectral data, including fluorescencespectral data.

FIG. 57 shows a graph 1321 depicting the weighted difference 1314between the mean reflectance values of mucus-obscured regions andunobscured regions of tissue as a function of wavelength 1316, accordingto an illustrative embodiment of the invention. The weighted differenceis as given in Equation 83. For the data sets used in FIG. 57, thewavelength providing the maximum value 1322 of D in Equation 83 is about577 nm. Thus, exemplary spectral characteristics identifiable with thisset of mucus-obscured “outlier” data include the reflectance spectraldata at about 577 nm, and any deviation of this data from reflectancespectral “tissue” data for unobscured regions of correspondingly similartissue at about 577 nm. This embodiment uses reflectance spectral data.Other embodiments may use other types of spectral data, includingfluorescence spectral data.

One illustrative embodiment comprises determining two wavelengths wherethe ratio of spectral data at the two wavelengths is most different forthe artifact-affected spectral data (the “outlier”) and spectral datafrom corresponding nearby tissue that is known to be unaffected by theartifact (the “tissue”). Alternatively, the method comprises determiningtwo wavelengths where the ratio of spectral data at the two wavelengthsweighted by a measure of variability is most different for the outlierdata and the tissue data. In one embodiment, the method comprisesdetermining a maximum value of D as a function of wavelength, where D isthe difference given in Equation 84 below:

$\begin{matrix}{{D = \frac{{{\mu\left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Outlier} - {\mu\left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Tissue}}}{\sqrt{{\sigma^{2}\left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Outlier} + {\sigma^{2}\left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Tissue}}}},} & (84)\end{matrix}$where μ(BB(λ)/BB(λ′))_(Outlier) is the mean of the ratios of reflectanceat wavelength λ and reflectance at wavelength λ′ for a set ofreflectance spectral data known to be affected by a given artifact,μ(BB(λ)/BB(λ′))_(Tissue) is the mean of the ratios of reflectance atwavelength λ and reflectance at wavelength λ′ for a set of reflectancespectral data that is known not to be affected by the given artifact,σ(BB(λ)/BB(λ′))_(Outlier) is the standard deviation of the ratios ofreflectance at wavelength λ and reflectance at wavelength λ′ for a setof reflectance spectral data known to be affected by the given artifact,and σ(BB(λ)/BB(λ′))_(Tissue) is the standard deviation of the ratios ofreflectance at wavelength λ and reflectance at wavelength λ′ for a setof reflectance spectral data known not to be affected by the givenartifact.

FIG. 58 shows a graph 1323 depicting a ratio of the weighted differences1324 between the mean reflectance values of glare-obscured regions andunobscured regions of tissue at two wavelengths, a numerator wavelength1326 and a denominator wavelength 1328, according to an illustrativeembodiment of the invention. The weighted difference 1324 is as given inEquation 84. For the data sets used in FIG. 58, the two wavelengthsproviding the maximum value of D in Equation 84 are about 401 nm(numerator) and about 404 nm (denominator). Thus, exemplary spectralcharacteristics identifiable with this set of glare-obscured “outlier”data include the ratio of reflectance spectral data at about 401 nm andthe reflectance spectral data at about 404 nm, as well as any deviationof this ratio from those of corresponding regions of similar butunobscured tissue. This embodiment uses reflectance spectral data. Otherembodiments may use other types of spectral data, including fluorescencedata.

FIG. 59 shows a graph 1325 depicting a ratio of the weighted differences1324 between the mean reflectance values of blood-obscured regions andunobscured regions of tissue at two wavelengths, a numerator wavelength1326 and a denominator wavelength 1328, according to an illustrativeembodiment of the invention. The weighted difference is as given inEquation 84. For the data sets used in FIG. 59, the two wavelengthsproviding the maximum value of D in Equation 84 are about 595 nm(numerator) and about 718 nm (denominator). Thus, an exemplary spectralcharacteristic identifiable with this set of blood-obscured “outlier”data includes the ratio of the reflectance spectral data at about 595 nmand the reflectance spectral data about 718 nm. This embodiment usesreflectance spectral data. Other embodiments may use other types ofspectral data, including fluorescence data.

FIG. 60 shows a graph 1327 depicting a ratio of the weighted differences1324 between the mean reflectance values of mucus-obscured regions andunobscured regions of tissue at two wavelengths, a numerator wavelength1326 and a denominator wavelength 1328, according to an illustrativeembodiment of the invention. The weighted difference is as given inEquation 84. For the data sets used in FIG. 60, the two wavelengthsproviding the maximum value of D in Equation 84 are about 545 nm(numerator) and about 533 nm (denominator). Thus, an exemplary spectralcharacteristic identifiable with this set of mucus-obscured “outlier”data includes the ratio of the reflectance spectral data at about 545 nmand the reflectance spectral data at about 533 nm. This embodiment usesreflectance spectral data. Other embodiments may use other types ofspectral data, including fluorescence data.

Another type of lighting artifact which may obscure spectral data isshadow, which may be caused, for example, by an obstruction blockingpart of the light from an illumination source on the optical probe 142of the embodiment apparatus. It may be important to differentiatebetween glare and shadow, so that spectral data representingunobstructed tissue can be properly identified. In an embodiment,broadband reflectance is expressed as the intensity of light diffuselyreflected from a region of the tissue, I_(t), over the intensity ofincident light, I_(o), at the region. When glare is measured in additionto light diffusely reflected from the tissue, a percentage of theoriginal intensity of incident light is included in the tissuereflectance measurement, so that the “reflectance” reading of a regionof a sample experiencing glare, R_(g)(λ), may be expressed as inEquation 85:R _(g)(λ)=(I _(t)(λ)+αI _(o)(λ))/I _(o)(λ),  (85)where α is a real number between 0.0 and 1.0; I_(t)(λ) is the intensityof light diffusely reflected from the region of tissue at wavelength λ,and I_(o)(λ) is the intensity of light incident on the region of thesample at wavelength λ. The intensity of the specularly-reflected lightis αI_(o)(λ). When the region of the sample is shadowed, only a portionof the incident intensity reaches the region. Thus, the “reflectance”reading of a region of a sample experiencing shadow, R_(s)(λ), may beexpressed as in Equation 86:R _(s)(λ)=βI _(t)(λ)/I _(o)(λ).  (86)where β is a real number between 0.0 and 1.0; I_(t)(λ) is the intensityof light at wavelength λ diffusely reflected from the region of tissuewith an incident light intensity of I_(o)(λ), and I_(o)(λ) is theintensity of light at wavelength λ that would be incident on the regionof the sample if unshadowed.

In one embodiment, the arbitration in step 128 of FIG. 1 comprisesdetermining if only one set of a pair of sets of spectral data isaffected by a lighting artifact, such as glare or shadow, each sethaving been obtained using light incident on the sample at a uniqueangle. If it is determined that only one set of a pair of sets ofspectral data is affected by the artifact, then the other set ofspectral data may be used in the determination of a characteristic ofthe region of the sample, for example. In one embodiment, it isdetermined that there is evidence of a lighting artifact in the spectraldata. Such evidence may be a large difference between the reflectancemeasurements of the two sets of spectral data. If such evidence exists,then one of the reflectance measurements will either be R_(g) or R_(s),as given by Equation 85 and Equation 86. In cases where members of onlyone set are affected by a lighting artifact, the remaining set ofreflectance measurements may be expressed as R, the intensity of lightdiffusely reflected from the region of the tissue, I_(t), divided by theintensity of light incident on the region of the tissue, I_(o). In anembodiment method, the larger of the two reflectance measurementscorresponding to a given wavelength is divided by the smaller. In caseswhere only one of the sets is affected by a lighting artifact, theresulting quotient will be either R_(g)/R, which is equal to1+αI_(o)(λ)/I_(t)(λ), or R/R_(s), which is equal to the constant, 1/β.If glare is present, the value of the quotient will depend on wavelengthand the plot of the quotient as a function of wavelength should looklike an inverted unobstructed tissue broadband signal because of theαI_(o)(λ)/I_(t)(λ) term. If shadow is present, the plot of the quotientshould be constant across the spectrum.

FIG. 61 shows a graph 1332 depicting as a function of wavelength 1336mean values and confidence intervals of a ratio 1334 of BB1 and BB2broadband reflectance spectral values (larger value divided by smallervalue) for regions confirmed as being either glare-obscured orshadow-obscured tissue, according to an illustrative embodiment of theinvention. The shadow points 1338 yield a nearly constant value, whilethe glare points 1340 vary over the range of wavelength 1336 in a mannerthat resembles the inverse of unobstructed tissue reflectance. Thus,FIG. 61 illustrates an embodiment in which it is determined whether onlyone set of a pair of sets of spectral data is affected by either glareor shadow, such that the other set is unaffected by glare or shadow andmay be used to determine a characteristic of the tissue, for example. Inan embodiment, the method comprises differentiating between glare andshadow by observing the steep slope of glare-affected reflectancespectral measurements between about 577 nm and 599 nm, for example,compared to the nearly flat slope of shadow-affected reflectancespectral measurements at those wavelengths, as seen in FIG. 61.

In one embodiment, the arbitration in step 128 of FIG. 1 includesapplying and/or developing spectral artifact classification rules(metrics) using spectral data, including one or more sets offluorescence and broadband reflectance data obtained using light at oneor more angles. In one embodiment, one set of fluorescence data and twosets of reflectance data are obtained from a given region of a tissuesample (interrogation point), where each of the two sets of reflectancedata are obtained using light incident on the region at a differentangle. These metrics determine what data is representative of a givenregion of tissue. By varying the metrics, desired levels of sensitivityand selectivity of a resulting tissue characterization usingtissue-representative data may be achieved.

The following metrics are applied in one embodiment of the arbitrationin step 128 of FIG. 1 and were determined using the embodimentsdiscussed above. These metrics were developed using one set offluorescence data and two sets of reflectance data, BB1 and BB2, forsamples of cervical tissue. Other embodiments use other combinations ofspectral data sets. Each of the two sets of reflectance data used in thefollowing metrics were obtained using light incident to a region of asample at different angles. An embodiment of the invention uses any orall of the metrics listed below to determine if any set of data shouldbe eliminated from use in determining a characteristic of a region oftissue, due to the presence of a spectral artifact. In an embodiment ofthe invention, wavelengths within a range of the wavelengths shown beloware used. In one embodiment, this range about the wavelengths is about±10 nm. In an embodiment of the invention, only certain parts of themetrics shown below are used. In one embodiment, only a portion of agiven set of spectral data are eliminated, not the entire set. In oneembodiment, BB1 and BB2 reflectance data are obtained, but fluorescencedata is not. Here, “eliminate data” means to eliminate data fromconsideration in an analysis, for example, an analysis to determine acondition of a region. It is possible to change sensitivity andselectivity of a tissue diagnostic algorithm by varying the metricsbelow, for instance by changing one or more of the threshold constants.Such variations are within an embodiment of this invention. The metricsfor one exemplary embodiment are as follows:

Glare Metric #1: Eliminate BB1 Data IF:

-   I. {BB1(419)>0.25 AND BB1(699)>0.51} OR BB1(529)/BB1(543)<1.0;-   OR II. Max{|ΔBB|/avgBB}(370–710)>0.25 AND BB1(419)>0.18 AND    BB1(699)>0.51 AND {BB1 (576)/BB2(576)}/{BB1 (599)/BB2(599)}>1.1;-   OR III. Max{|ΔBB1/avgBB}(370–710)>0.4 AND    {BB1(576)/BB2(576)}/{BB1(599)/BB2(599)}>1.1 AND BB2(699)>0.3.    Glare Metric #2: Eliminate BB2 Data IF:-   I. {BB2(419)>0.25 AND BB2(699)>0.51} OR BB2(529)/BB2(543)<1.0;-   OR II. Max{|ΔBB|/avgBB}(370–710)>0.25 AND BB2(419)>0.18 AND    BB2(699)>0.51 AND {BB2(576)/BB1(576)}/{BB2(599)/BB1(599)}>1.1;-   OR III. Max{|ΔBB|/avgBB}(370–710)>0.4 AND    {BB2(576)/BB1(576)}/{BB2(599)/BB1(599)}>1.1 AND BB1(699)>0.3.    Shadow Metric #1: Eliminate BB1 Data IF:-   I. BB2(499)>BB1(499) AND Max{|ΔBB|/avgBB}(370–710)>0.25 AND    BB1(499)<0.05;-   OR II. Max{|ΔBB|/avgBB}(370–710)>0.5 AND {BB1    (576)/BB2(576)}/{BB1(599)/BB2(599)}<1.1 AND BB2(576)>BB1(576) AND    BB1(419)<0.2.    Shadow Metric #2: Eliminate BB2 Data IF:-   I. BB1(499)>BB2(499) AND Max{|ΔBB|/avgBB}(370–710)>0.25 AND    BB2(499)<0.05;-   OR II. Max{|ΔBB|/avgBB}(370–710)>0.5 AND    {BB2(576)/BB1(576)}/{BB2(599)/BB1(599)}<1.1 AND BB1(576)>BB2(576)    AND BB2(419)<0.2.    Low Signal: Eliminate BB1, BB2, and Fl Data IF:-   I. Fl(479)<3.5 counts/μJ (where mean fluorescent intensity of normal    squamous tissue is about 70 counts/μJ at about 450 nm);-   OR II. BB1(499)<0.035 & BB2(499)<0.035.    where BB1(X) is the BB1 reflectance spectrum measurement at    wavelength X, BB2(X) is the BB2 reflectance spectrum measurement at    wavelength λ, Max{|ΔBB|/avgBB}(370–710) indicates the maximum of    {the absolute value of the difference between the BB1 and BB2    reflectance spectrum measurements divided by the average of the BB1    and BB2 measurements at a given wavelength} over the range of about    370 to 710 nm, and Fl(X) is the fluorescence spectrum measurement at    wavelength X. The following are notes regarding the Metrics listed    above and apply to a preferred embodiment, subject to the variations    described above:    Glare Metric #1 and Glare Metric #2:-   Level I: Broadband measurements are generally greater than about    0.25 at about 419 nm and greater than about 0.51 at about 699 nm    only when there is glare in the channel (i.e. BB1 or BB2). The lack    of a downward slope between about 499 and about 543 nm is also a    strong indication that the broadband measurements are affected by    glare.-   Level II: Large percentage differences in the broadband measurements    combined with higher than average reflectance at about 419 nm and    about 699 nm also indicates the presence of glare. The presence of a    slope when the broadband measurements at about 576 nm and about 599    nm are divided is further confirmation that glare is present.-   Level III: A maximum broadband percent difference that is larger    than about 0.4 indicates that there is a lighting artifact present.    The presence of a slope when the broadband measurements at about 576    and about 599 nm are divided and an off-channel broadband greater    than about 0.3 at about 699 nm reveals that the lighting artifact is    due to glare instead of shadow.-   If a point is identified as glare in one channel, then subsequently    identified as glare in both channels, both broadband measurements    should be eliminated.    Shadow Metric #1 and Shadow Metric #2:-   Level I: Broadband measurements that are shadowed generally will    have a large percent difference between BB1 and BB2 and a low    reflectance at about 499 nm.-   Level II: A maximum broadband percent difference that is larger than    about 0.5 indicates that there is a lighting artifact present.    Lacking a large slope when the broadband measurements at about 576    and about 599 nm are divided and an off-channel broadband less than    about 0.2 at about 419 nm reveals that the point is shadow instead    of glare.    Cases Where Both BB and Fl Measurements Should be Eliminated:    Low Signal:-   Broadband measurements lower than about 0.035 at about 449 nm or    fluorescence measurements lower than about 3.5 at about 479 nm    indicate that the measurements are not coming from tissue, but    rather from blood, the os, smoke tube, speculum, or another    obstruction. Sites with significant shadowing in both broadband    channels are also identified with this metric. Because of the    uncertainty of the tissue being measured, the reflectance and    fluorescence data from that point are assumed invalid, regardless of    whether it was identified by fluorescence or the broadband channels.-   The low signal metric acts as a hard mask because it eliminates a    qualifying interrogation point from consideration by the classifier    or the other masks, such as the spectral masks in step 130 of    FIG. 1. The low signal metric acts as a hard mask, for example, for    points that have shadowing in both BB1 and BB2.

The metrics used in this embodiment of step 128 of FIG. 1 include a lowsignal metric, which detects spectral data affected by obstructionartifacts such as blood, a speculum, a smoke tube, or other obstruction.This metric also identifies regions where both sets of broadbandreflectance data are affected by shadow. These were combined into onelow signal metric in this embodiment, since regions affected by theseartifacts exhibit similar characteristics, such as low fluorescence andlow broadband reflectance measurements.

FIG. 62 shows a graph 1342 depicting broadband reflectance 1344 as afunction of wavelength 1346 for the BB1 channel 1348 and the BB2 channel1350 measurements for a region of tissue where the BB1 data is affectedby glare but the BB2 data is not, according to an illustrativeembodiment of the invention. The glare leads to a higher value ofreflectance 1344 than that of surrounding unaffected tissue. By applyingthe metrics listed above in step 128 of FIG. 1, it is determined thatthe exemplary BB1 set of spectral data shown in FIG. 62 is affected byglare and is thus not suitably representative of this region of thetissue sample. Applying the metrics of step 128 also determines that theBB2 set of spectral data is potentially representative of this region ofthe sample (unaffected by an artifact), since it is not eliminated. Oneembodiment comprises using this representative data in step 132 of FIG.1 to determine a condition of this region of the sample, for example,the state of health.

FIG. 63 shows a graph 1351 depicting broadband reflectance 1344 as afunction of wavelength 1346 for the BB1 channel 1352 and the BB2 channel1354 broadband reflectance spectral data for a region of tissue wherethe BB2 data is affected by shadow but the BB1 data is not, according toan illustrative embodiment of the invention. The shadow leads to a lowervalue of reflectance 1344 than that of surrounding unaffected tissue. Byapplying the metrics listed above in step 128 of FIG. 1, it isdetermined that the exemplary BB2 set of spectral data shown in FIG. 63is affected by shadow and is therefore not suitably representative ofthis region of the tissue sample. Applying the metrics of step 128 alsoleads to the determination that the BB1 set of spectral data ispotentially representative of this region of the sample, since the BB1set of data is not eliminated. One embodiment comprises using thisrepresentative data in step 132 of FIG. 1 to determine a condition ofthis region of the sample, for example, the state of health.

FIG. 64 shows a graph 1358 depicting broadband reflectance 1360 as afunction of wavelength 1362 for the BB1 channel 1364 and the BB2 channel1366 measurements for a region of tissue that is obscured by blood,according to an illustrative embodiment of the invention. By applyingthe metrics listed above, it is determined that blood is present, andthat both the BB1 and the BB2 sets of spectral data are consideredunrepresentative of this region of the tissue sample.

FIG. 65 shows a graph 1367 depicting broadband reflectance 1360 as afunction of wavelength 1362 for the BB1 channel 1368 and the BB2 channel1370 measurements for a region of tissue that is unobscured, accordingto an illustrative embodiment of the invention. Applying this methoddetermines that neither set of spectral data is affected by an artifact,and, therefore, either is representative of the tissue sample. Oneembodiment comprises using an average value 1372 of the BB1 and BB2measurements at each wavelength to represent the region of the tissuesample in determining a condition of this region, for example, the stateof health of the region, in step 132 of FIG. 1.

Application of the metrics listed above was performed using varioustissue types to verify the sensitivity and specificity of the metrics.While, in one embodiment, it is undesirable to eliminate good spectraldata of normal tissue, it is worse to eliminate good spectral data ofdiseased tissue, particularly if it is desired to use the data in theclassification of the state of health of a region of tissue. Thefollowing tissue types were used in the verification: tt-132 (metaplasiaby impression), tt-155 (normal by impression), tt-117 (blood), NEDpath(no evidence of disease confirmed by pathology), and cin23all (CIN 2/3diseased tissue). Table 5 shows the number of points (regions)corresponding to each of these tissue types, the determinations from themetrics listed above for these points, and the number of points whereone set of broadband reflectance spectral data were eliminated, whereboth sets of broadband reflectance spectral data were eliminated, andwhere both reflectance and fluorescence spectral data were eliminated.

TABLE 5 Verification of Metrics Tissue Type cin23all nedpath tt-117tt-132a tt-155 Total pts. 477 919 175 5000 2016 Low Signal 2 14 126 2 0Glare in BB1 7 30 4 122 26 Glare in BB2 9 40 9 134 16 Glare in both 3 51 15 5 Shadow in BB1 47 35 4 165 132 Shadow in BB2 16 37 24 359 32 OneBB Removed (%) 16.6 15.5 23.4 15.6 10.2 Both BB Removed (%) 1.05% 2.07%72.57% 0.34% 0.25% FI Removed (%) 0.42 1.52 72.00 0.04 0.00

For the regions (points) corresponding to CIN 2/3 diseased tissue, nobroadband reflectance measurements were unnecessarily eliminated fromthe set using the above metrics. The points identified as being lowsignal were all located on the os. All points that were identified bythe metric as shadow were verified as being correct, and only one pointidentified as glare was incorrect.

For the nedpath points (no evidence of disease), only two tissue pointswere unnecessarily eliminated after being misidentified as mucus. Apoint that was actually dark red tissue with glare was incorrectlyidentified as shadow in BB2. The points that were identified as glarewere verified as being correct.

Out of the 175 blood points, 126 were identified as being low signal.The glare points and shadow points were accurate.

Out of the 5000 points in the metaplasia by impression group, there wereno valid tissue points lost. The data set was improved by eliminatingabout 800 readings of points affected by either glare or shadow.

Out of the 2016 normal by impression points, no measurements wereunnecessarily removed from the set.

FIG. 66 shows a graph 1374 depicting the reduction in the variability ofbroadband reflectance measurements 1376 of CIN 2/3-confirmed tissueproduced by filtering (eliminating non-representative spectral data)using the metrics of step 128 in FIG. 1 described above, according to anillustrative embodiment of the invention. The graph 1374 depicts meanvalues and standard deviations of broadband reflectance spectral databefore and after filtering.

FIG. 67 shows a graph 1378 depicting the reduction in the variability ofbroadband reflectance measurements 1376 of tissue classified as “noevidence of disease confirmed by pathology” produced by filtering usingthe metrics described above, according to an illustrative embodiment ofthe invention. The graph 1378 depicts mean values and standarddeviations of broadband reflectance spectral data before and afterfiltering.

FIG. 68 shows a graph 1380 depicting the reduction in the variability ofbroadband reflectance measurements 1376 of tissue classified as“metaplasia by impression” produced by filtering using the metricsdescribed above, according to an illustrative embodiment of theinvention. The graph 1380 depicts mean values and standard deviations ofbroadband reflectance spectral data before and after filtering.

FIG. 69 shows a graph 1382 depicting the reduction in the variability ofbroadband reflectance measurements 1376 of tissue classified as “normalby impression” produced by filtering using the metrics described above,according to an illustrative embodiment of the invention. The graph 1382depicts mean values and standard deviations of broadband reflectancespectral data before and after filtering.

FIG. 70A depicts an exemplary image of cervical tissue 1388 divided intoregions for which two types of reflectance spectral data and one type offluorescence spectral data are obtained, according to one embodiment ofthe invention. FIG. 70B is a representation 1398 of the regions depictedin FIG. 70A and shows the categorization of each region using themetrics in step 128 of FIG. 1. The black-highlighted sections 1390 ofthe image 1388 in FIG. 70A correspond to points (regions) that had bothreflectance measurements eliminated by application of the embodimentmethod. Many of the lower points 1392, as seen in both FIGS. 70A and70B, are in shadow because the speculum obstructs the view of one of thechannels. Glare is correctly identified prominently at the upper oneo'clock position 1394. Since there are blood points on the shadowedsection, some are labeled blood (low signal) and others are treated asshadow.

FIG. 71A depicts an exemplary image of cervical tissue 1402 divided intoregions for which two types of reflectance spectral data and one type offluorescence spectral data are obtained, according to one embodiment ofthe invention. FIG. 71B is a representation 1406 of the regions depictedin FIG. 71A and shows the categorization of each region using themetrics in step 128 of FIG. 1. FIGS. 71A and 71B show an example of acervix that has a large portion of the lower half 1404 affected byshadow. However, only one of the sets of reflectance spectral data (BB2)is affected by the shadow artifact. The BB1 reflectance spectral data isnot affected by shadow. Applying the metrics above, the BB1 data areused to describe these regions, while the BB2 data are eliminated fromconsideration. The accuracy of tissue characterization using thereflectance measurements should be improved significantly for thispatient using the arbitration metrics of step 128 of FIG. 1, since themore accurate broadband measurements will be used in latercharacterization steps instead of simply averaging the two broadbandmeasurements, which would skew the measurements due to a lightingartifact.

FIG. 72A depicts an exemplary image of cervical tissue 1410 divided intoregions for which two types of reflectance spectral data and one type offluorescence spectral data are obtained, according to an illustrativeembodiment of the invention. FIG. 72B is a representation 1416 of theregions depicted in FIG. 72A and shows the categorization of each regionusing the metrics in step 128 of FIG. 1. FIGS. 72A and 72B show an imagewith a portion 1412 that is shadowed and off of the cervix. Due to anobstruction from the smoke tube in the upper part of the image, thereare many low signals. Even though much of the cervix is shadowed in BB11414, there are still some BB2 and fluorescence readings usable in latertissue classification steps.

Classification System Overview

The tissue characterization system 100 of FIG. 1 combines spectral dataand image data obtained by the instrument 102 to characterize states ofhealth of regions of a tissue sample. In one embodiment, the spectraldata are first motion-tracked 106, preprocessed 114, and arbitrated 128before being combined with image data in step 132 of FIG. 1. Likewise,in one embodiment, the image data are first focused 122 and calibrated124 before being combined with spectral data in step 132 of FIG. 1. Eachof these steps are discussed in more detail herein.

FIG. 73 shows how spectral data and image data are combined in thetissue characterization system of FIG. 1, according to one embodiment.The block diagram 1420 of FIG. 73 depicts steps in processing andcombining motion-tracked 106, preprocessed 114, and arbitrated 128spectral data with focused 122, calibrated 124 image data to determinestates of health of regions of a tissue sample. After preprocessing 114,spectral data from each of the interrogation points (regions) of thetissue sample are arbitrated in step 128 of FIG. 73. In the embodimentshown, a fluorescence spectrum, F, and two broadband reflectancespectra, BB1 and BB2, are used to determine one representativereflectance spectrum, BB, used along with the fluorescence spectrum, F,for each interrogation point. This is depicted in FIG. 73 as three heavyarrows representing the three spectra—BB1, BB2, and F—enteringarbitration block 128 and emerging as two spectra—BB and F. Block 128 ofFIG. 73 also applies an initial low-signal mask as a first pass atidentifying obscured interrogation points, discussed previously herein.

In the embodiment of FIG. 73, the arbitrated broadband reflectancespectrum, BB, is used in the statistical classification algorithm 134,while both the broadband reflectance spectrum, BB, and the fluorescencespectrum, F, as well as the image data, are used to determineheuristic-based and/or statistics-based metrics, or “masks”, forclassifying the state of health of tissue at interrogation points.Masking can be a means of identifying data that are potentiallynon-representative of the tissue sample. Potentially non-representativedata includes data that may be affected by an artifact or obstructionsuch as blood, mucus, fluid, glare, or a speculum. Such data is eitherhard-masked or soft-masked. Hard-masking of data includes identifyinginterrogation points at which the data is not representative ofunobscured, classifiable tissue. This results in a characterization of“Indeterminate” at such an interrogation point, and no furthercomputations are necessary for that point. Soft-masking includesapplying a weighting function or weighting factor to identified,potentially non-representative data. The weighting is taken into accountduring calculation of disease probability and may or may not result inan indeterminate diagnosis at the corresponding tissue region.Soft-masking provides a means of weighting spectral and/or image dataaccording to the likelihood that the data is representative of clear,unobstructed tissue in a region of interest. In the embodiment shown inFIG. 73, both hard masks and soft masks are determined using acombination of spectral data and image data. Furthermore, the masks ofFIG. 73 use spectral and image data to identify interrogation pointsthat are not particularly of interest in the exam, such as the vaginalwall, smoke tube tissue, the os, or tissue outside the region ofinterest.

In addition to determining data that are potentially non-representativeof regions of interest, the masks shown in FIG. 73 also include masksthat determine where the data is highly indicative of necrotic tissue ordisease-free (NED) tissue. It has been discovered that necrotic tissueand disease-free tissue are often more predictably determined by using aheuristic metric instead of or in combination with a statisticalclassifier than by using a statistical classifier alone. For example,one embodiment uses certain values from fluorescence spectra todetermine necrotic regions, since fluorescence spectra can indicate theFAD/NADH component and porphyrin component of necrotic tissue. Also, anembodiment uses prominent features of fluorescence spectra indicative ofnormal squamous tissues to classify tissue as “NED” (no evidence ofdisease) in the spectral mask.

Identifying necrotic and NED regions at least partially by usingheuristic metrics allows for the development of statistical classifiers134 that concentrate on differentiating tissue less conducive toheuristic classification—for example, statistical classifiers thatdifferentiate high grade cervical intraepithelial neoplasia (i.e. CIN2/3) from low grade neoplasia (i.e. CIN 1) and healthy tissue.

In FIG. 73, step 130 uses the arbitrated spectra, BB and F, to determinefour spectral masks—NED_(spec) (no evidence of disease),Necrosis_(spec), [CE]_(spec) (cervical edge/vaginal wall), and[MU]_(spec) (mucus/fluid). The focused, calibrated video data is used todetermine nine image masks—Glare_(vid), Mucus_(vid), Blood_(vid),Os_(vid), [ROI]_(vid) (region of interest), [ST]_(vid) (smoke tube),[SP]_(vid) (speculum), [VW]_(vid) (vaginal wall), and [FL]_(vid) (fluidand foam). Step 1422 of FIG. 73 combines these masks to produce a hard“indeterminate” mask, a soft “indeterminate” mask, a mask identifyingnecrotic regions, and a mask identifying healthy (NED) regions. In theembodiment of FIG. 73, steps 1424 and 1426 apply the necrotic mask andhard “indeterminate” mask, respectively, prior to using the broadbandspectral data in the statistical classifiers 134, while steps 1428 and1430 apply the soft “indeterminate” mask and the NED mask after thestatistical classification step 134.

The embodiment shown in FIG. 73 can classify each interrogation point instep 1432 as necrotic, CIN 2/3, NED, or Indeterminate. There may be somepost-classification processing in step 1434, for example, forinterrogation points having a valid fluorescence signal but having bothbroadband signals, BB1 and BB2, eliminated by application of thearbitration metrics in step 128. The embodiment in FIG. 73 then uses thefinal result to create a disease display overlay of a reference image ofthe tissue sample in step 138. Each of the masking and classificationsteps summarized above are discussed in more detail herein.

In one alternative embodiment, the statistical classifiers in step 134of FIG. 73 additionally include the use of fluorescence, image, and/orkinetic data. One alternative embodiment includes using different setsof spectral and/or image masks than those in FIG. 73. Also, onealternative embodiment includes using a different order of applicationof heuristic masks in relation to one or more statistical classifiers.In one alternative embodiment, kinetic data is determined by obtainingintensity data from a plurality of images captured during a tissue scan,determining a relationship between corresponding areas of the images toreflect how they change with time, and segmenting the images based onthe relationship. For example, an average kinetic whitening curve may bederived for tissue areas exhibiting similar whitening behavior.Whitening kinetics representative of a given area may be compared toreference whitening kinetics indicative of known states of health,thereby indicating a state of health of the given area. In onealternative embodiment, the kinetic image-based data may be combinedwith spectral data to determine states of health of regions of a tissuesample.

FIG. 74 shows a block diagram 1438 depicting steps in the method of FIG.73 in further detail. The steps of FIG. 74 are summarized below and arediscussed in detail elsewhere herein. Steps 1440, 1442, 1444, and 1446in FIG. 74 depict determination of the spectral masks from thearbitrated broadband reflectance and fluorescence signals, as seen instep 130 of FIG. 73. Steps 1448, 1450, 1452, 1454, 1456, 1458, 1460,1462, and 1464 in FIG. 74 depict determination of the image masks fromthe focused, calibrated video data, as seen in step 108 of FIG. 73. Thelines extending below these mask determination steps in FIG. 74 show how(in one embodiment) the masks are combined together, as indicated instep 1422 of FIG. 73. Steps 1466, 1468, 1470, 1472, 1474, 1476, 1478,and 1480 of FIG. 74 shows which masks are combined. Also important isthe manner in which the masks are combined, disclosed in the detailedstep explanations herein.

The statistical classification step 134 from FIG. 73 is shown in FIG. 74as steps 1482, 1484, and 1486. Here, the pictured embodiment applies anecrosis mask 1424 and a hard “indeterminate” mask 1426 to thearbitrated broadband spectral data to eliminate the need to furtherprocess certain necrotic and indeterminate interrogation points in theclassification step. Classification includes processing of broadbandspectral data via wavelength region truncation, wavelength subsampling,and/or mean-centering. The processed data is then used in two differentfeature extraction methods. These include a principal component analysis(PCA) method used in the DASCO classifier step 1484 (DiscriminantAnalysis with Shrunken Covariances) and a feature coordinate extraction(FCE) method used in the DAFE classifier step 1482 (DiscriminantAnalysis Feature Extraction). Each of steps 1484 and 1482 extract alower dimensional set of features from the spectral data that is thenused in a Bayes' classifier to determine probabilities of classificationin one or more tissue-class/state-of-health categories. Theclassification probabilities determined in steps 1482 and 1484 arecombined in step 1486. Each of the classifiers in steps 1482 and 1484are specified by a set of parameters that have been determined bytraining on known reference data. One embodiment includes updating theclassifier parameters as additional reference data becomes available.

Spectral Masking

The invention comprises determining spectral masks. Spectral masksidentify data from a patient scan that are potentiallynon-representative of regions of interest of the tissue sample. Spectralmasks also identify data that are highly indicative of necrotic tissueor normal squamous (NED) tissue. In one embodiment, the spectral masksare combined as indicated in the block flow diagram 1438 of FIG. 74, inorder to account for the identification of spectrally-maskedinterrogation points in the tissue-class/state-of-health classificationstep 1432. Steps 1440, 1442, 1444, and 1446 in FIG. 74 depict thedetermination of spectral masks from the arbitrated broadbandreflectance and fluorescence spectra obtained during a patient scan andare discussed in more detail below.

Step 1440 in FIG. 74 depicts the determination of an NED_(spec) (noevidence of disease) spectral mask using data from the fluorescencespectrum, F, and the broadband reflectance spectrum, BB, at each of theinterrogation points of the scan pattern, following the arbitration andlow-signal masking step 128. Applying the NED_(spec) mask reduces falsepositive diagnoses of CIN 2/3 resulting from thetissue-class/state-of-health classification step 134 in FIG. 1 (and FIG.89). The NED_(spec) mask identifies tissue having optical propertiesdistinctly different from those of CIN 2/3 tissue. More specifically, inone embodiment, the NED_(spec) mask uses differences between thefluorescence signals seen in normal squamous tissue and CIN 2/3 tissue.These differences are not accounted for by tissue-class/state-of-healthclassifiers based on broadband reflectance data alone. For example, theNED_(spec) mask uses the collagen peak seen in the fluorescence spectraof normal squamous tissue at about 410 nm to distinguish normal squamoustissue from CIN 2/3 tissue.

FIG. 75 shows a scatter plot 1500 depicting discrimination betweenregions of normal squamous tissue and CIN 2/3 tissue for a set of knownreference data, according to one embodiment. Plotting fluorescenceintensity at 460 nm (y-axis, 1502) against a ratio of fluorescenceintensity, F(505 nm)/F(410 nm), (x-axis, 1504) provides gooddiscrimination between regions known to be normal squamous tissue (bluepoints in FIG. 75) and regions known to be CIN 2/3 tissue (red points inFIG. 75). One component of the NED_(spec) discrimination metric is shownby line 1506 in FIG. 75, which divides a region of the plot that ispredominately representative of normal squamous tissue (1508) from aregion of the plot that is predominately representative of CIN 2/3tissue (1510). The divider 1506 can be adjusted, for example, to furtherreduce false positives or to allow detection of more true positives atthe expense of increased false positives.

In one embodiment, the fluorescence over reflectance ratio at about 430nm is also included in the NED_(spec) metric to determine normalcolumnar tissue sites that may not be identified by the component of themetric illustrated in FIG. 75 (i.e. blue points on the right of line1506). It is found that fluorescence of CIN 2/3 tissue at about 430 nmis lower relative to normal tissue, while CIN 2/3 reflectance at about430 nm is higher relative to normal tissue, after application of acontrast agent such as acetic acid.

FIG. 76 shows a graph 1512 depicting as a function of wavelength 1514the mean broadband reflectance values 1516 for a set of known normalsquamous tissue regions 1518 and a set of known CIN 2/3 tissue regions1520, used in one embodiment to determine an additional component of theNED_(spec) spectral mask. FIG. 77 shows a graph 1522 depicting as afunction of wavelength 1524 the mean fluorescence intensity values 1526for the set of known squamous tissue regions 1528 and the set of knownCIN 2/3 tissue regions 1530. The difference between curves 1528 and 1530in FIG. 77 is pronounced. Thus, a term is included in the NED_(spec)metric based on the best ratio of wavelengths found to maximize valuesof D in the discrimination equation, Equation 87, below:

$\begin{matrix}{D = \frac{{{\mu\left( {{F(\lambda)}/{F\left( \lambda^{\prime} \right)}} \right)}_{Outlier} - {\mu\left( {{F(\lambda)}/{F\left( \lambda^{\prime} \right)}} \right)}_{Tissue}}}{\sqrt{{\sigma^{2}\left( {{F(\lambda)}/{F\left( \lambda^{\prime} \right)}} \right)}_{Outlier} + {\sigma^{2}\left( {{F(\lambda)}/{F\left( \lambda^{\prime} \right)}} \right)}_{Tissue}}}} & (87)\end{matrix}$where μ indicates mean and σ indicates standard deviation. FIG. 78 showsa graph 1532 depicting values of D in Equation 87 using a range ofnumerator wavelengths 1536 and denominator wavelengths 1538. Accordingto the graph 1532 in FIG. 78, values of D are maximized using thefluorescence ratio F(450 nm)/F(566 nm). Alternately, other combinationsof numerator wavelength and denominator wavelength may be chosen.

A scatter plot depicting discrimination between regions of normalsquamous tissue and CIN 2/3 tissue for a set of known reference data areproduced by comparing the ratio F(450 nm)/F(566 nm) to a thresholdconstant. Then, a graph of true positive ratio (TPR) versus falsepositive ratio (FPR) in the discrimination between regions of normalsquamous tissue and CIN 2/3 tissue are obtained using a thresholdconstant. For example, a TPR of 65% and an FPR of 0.9% is obtained usinga threshold constant of 4.51. The ratio of false positives may bereduced by adjusting the threshold.

Therefore, in one embodiment, the NED_(spec) mask combines the followingthree metrics:F(430)/BB(430)>x₁  (88)F(450)/F(566)>x₂  (89)F(460)>x₃·F(505)/F(410)−x₄  (90)where x₁, x₂, x₃, and x₄ are constants chosen based on the desiredaggressiveness of the metric. Equations 88–90 account for thedistinguishing features of spectra obtained from regions of normalsquamous tissue versus spectra from CIN 2/3 tissue regions, as discussedabove.

FIGS. 79A–D illustrate adjustment of the components of the NED_(spec)mask metric shown in Equations 88, 89, and 90. FIG. 79A depicts areference image of cervical tissue 1554 from a patient scan in whichspectral data is used in arbitration step 128, in NED_(spec) spectralmasking, and in statistical classification of interrogation points ofthe tissue sample. FIG. 79B is a representation (obgram) 1556 of theinterrogation points (regions) of the tissue sample depicted in thereference image 1554 of FIG. 79A and shows points that are “masked”following application of Equation 90. The obgram 1556 of FIG. 79B showsthat some additional interrogation points are masked as NED tissue byadjusting values of x₃ and x₄ in Equation 90 from {x₃=120, x₄=42} to{x₃=115, x₄=40}. FIG. 79C shows interrogation points that are “masked”following application of Equation 89. The obgram 1570 of FIG. 79C showsthat a few additional points are masked as NED tissue by adjusting thevalue of x₂ from 4.0 to 4.1. FIG. 79D shows interrogation points thatare masked following application of Equation 88. The obgram 1584 of FIG.79D shows that a few additional points are masked as NED tissue byadjusting the value of x₁ from 610 to 600.

In one embodiment values of x₁, x₂, x₃, and x₄ in Equations 88, 89, and90 are determined using multidimensional unconstrained nonlinearminimization. In one embodiment, the overall NED_(spec) metric thatresults is as follows:F(430)/BB(430)>600 ct/μJ ORF(450)/F(566)>4.1 ORF(460)>115·F(505)/F(410)−40where the mean fluorescent intensity of normal squamous tissue is about70 counts/μJ at about 450 nm.

Step 1442 in FIG. 74 depicts the determination of Necrosis_(spec), anecrotic tissue spectral mask, using data from the fluorescencespectrum, F, at each of the interrogation points of the scan pattern,following the arbitration and low-signal masking step 128. Unlike theother spectral masks (steps 1440, 1442, and 1446 in FIG. 74), which aredesigned to reduce false positive diagnoses of CIN 2/3, theNecrosis_(spec) mask identifies areas of necrotic tissue, therebyidentifying patients with fairly advanced stages of invasive carcinoma.

In one embodiment, the Necrosis_(spec) mask uses prominent features ofthe fluorescence spectra from a set of known necrotic regions toidentify necrotic tissue. For example, in one embodiment, theNecrosis_(spec) mask uses the large porphyrin peaks of necrotic tissueat about 635 nm and/or at about 695 nm in identifying necrotic tissue.FIG. 80 shows a graph 1598 depicting fluorescence intensity 1600 as afunction of wavelength 1602 from an interrogation point confirmed asinvasive carcinoma by pathology and necrotic tissue by impression, whileFIG. 81 shows a graph 1612 depicting broadband reflectance spectra BB1and BB2 for the same point.

The graph 1598 of FIG. 80 shows the distinctive porphyrin peaks atreference numbers 1604 and 1606. Concurrent with high porphyrinfluorescence at necrotic regions is a smaller peak at about 510 nm(label 1608), possibly due to flavin adenine dinucleotide (FAD), with anintensity greater than or equal to that of nicotinamide adeninedinucleotide (NADH) at about 450 nm (label 1610). The FAD/NADH ratio isa measure of ischemia and/or hypoxia indicative of advanced stages ofcancer.

Thus, in one embodiment, the overall Necrosis_(spec) metric has one ormore components indicative of FAD/NADH and one or more componentsindicative of porphyrin. In one embodiment, the Necrosis_(spec) metricis as follows:F(510 nm)/F(450 nm)>1.0 ANDF(635 nm)/F(605 nm)>1.3 ANDF(635 nm)/F(660 nm)>1.3 ANDF(635 nm)>20 ct/μJwhere mean fluorescent intensity of normal squamous tissue is about 70counts/μJ at about 450 nm, and where the first line of the metricindicates FAD/NADH (FAD) and the remainder of the metric indicatesporphyrin. This metric requires all components to be satisfied in orderfor a region of tissue to be classified as necrotic. In one embodiment,the combination is needed to reduce false necrosis diagnoses inpatients. The presence of porphyrin does not always indicate necrosis,and necrosis masking based solely on the detection of porphyrin mayproduce an unacceptable number of false positives. For example,porphyrin may be present due to hemoglobin breakdown products followingmenses or due to systemic porphyrin resulting from medications,bacterial infection, or porphyria. Thus, the presence of both porphyrinand the indication of FAD must both be determined in order for a regionto be identified as necrotic by the Necrosis_(spec) metric in theembodiment described above.

FIG. 82A depicts a reference image 1618 of cervical tissue from the scanof a patient confirmed as having advanced invasive cancer, in whichspectral data is used in arbitration step 128, in Necrosis_(spec)spectral masking, and in statistical classification 134 of interrogationpoints of the tissue sample. FIG. 82B is an obgram 1620 of theinterrogation points (regions) of the tissue sample depicted in FIG. 82Aand shows points that are identified by application of the FAD componentof the Necrosis_(spec) metric above (1628), as well as points that areidentified by application of the porphyrin component of theNecrosis_(spec) metric above (1626). The overall Necrosis_(spec) maskabove identifies points as necrotic only when both FAD and porphyrin areidentified. In FIG. 82B, interrogation points that are marked by both ablue dot (FAD 1626) and a green ring (porphyrin 1626) are identified asnecrotic tissue by application of the Necrosis_(spec) metric above.

Step 1444 in FIG. 74 depicts the determination of a cervicaledge/vaginal wall spectral mask ([CE]_(spec)) using data from thefluorescence spectrum, F, and the broadband reflectance spectrum, BB, ofeach interrogation point of a scan, following the arbitration andlow-signal masking step 128. The [CE]_(spec) mask identifies low-signaloutliers corresponding to the cervical edge, os, and vaginal wall,which, in one embodiment, are regions outside an area of diagnosticinterest for purposes of the tissue characterization system 100 of FIG.1.

FIGS. 83, 84, 85, and 86 compare broadband reflectance and fluorescencespectra of cervical edge and vaginal wall regions to spectra of CIN 2/3tissue. In one embodiment, these comparisons are used in adiscrimination analysis to determine a [CE]_(spec) spectral mask. FIG.83 shows a graph 1638 depicting as a function of wavelength 1640 themean broadband reflectance values 1642 for a set of known cervical edgeregions 1644 and a set of known CIN 2/3 tissue regions 1646. FIG. 84shows a graph 1648 depicting as a function of wavelength 1650 the meanfluorescence intensity values 1652 for the set of known cervical edgeregions 1654 and the set of known CIN 2/3 tissue regions 1656. FIG. 85shows a graph 1658 depicting as a function of wavelength 1660 the meanbroadband reflectance values 1662 for a set of known vaginal wallregions 1664 and a set of known CIN 2/3 tissue regions 1666. FIG. 86shows a graph 1668 depicting as a function of wavelength 1670 the meanfluorescence intensity values 1672 for the set of known vaginal wallregions 1674 and the set of known CIN 2/3 tissue regions 1676.

In one embodiment, features of the curves in FIGS. 83, 84, 85, and 86are used in determining the [CE]_(spec) spectral mask metric. Forexample, from FIGS. 84 and 86, it is seen that reflectance values forcervical edge/vaginal wall regions are lower than CIN 2/3 reflectance,particularly at about 450 nm and at about 700 nm. From FIGS. 84 and 86,it is seen that there is a “hump” in the fluorescence curves forcervical edge regions 1654 and vaginal wall regions 1674 at about 400nm, where there is no such hump in the CIN 2/3 curve (1656/1676). Thiscauses the ratio of fluorescence intensity, F(530 nm)/F(410 nm), to below at cervical edge/vaginal wall regions, relative to that of CIN 2/3regions. From FIG. 86, the mean fluorescence intensity of vaginal wallregions 1674 is lower than that of CIN 2/3 regions at least from about500 nm to about 540 nm. In one embodiment, these observations arecombined to determine the overall [CE]_(spec) mask metric as follows:BB(450 nm)·BB(700 nm)/BB(540 nm)<0.30 ORF²(530 nm)/F(410 nm)<4.75.The top line of the metric above reflects the observation that the meanreflectance of cervical edge/vaginal wall tissue is comparable to thatof CIN 2/3 tissue at about 540 nm and lower than that of CIN 2/3 tissueat about 450 nm and about 700 nm. The bottom line of the metric abovereflects the observation that the fluorescence of a cervicaledge/vaginal wall region may have a lower fluorescence at 530 nm thanCIN 2/3 tissue and that the cervical edge/vaginal wall region may have alower F(530 nm)/F(410 nm) ratio than CIN 2/3 tissue.

FIG. 87A depicts a reference image 1678 of cervical tissue from apatient scan in which spectral data is used in arbitration and[CE]_(spec) spectral masking. FIG. 87B is an obgram 1680 of theinterrogation points (regions) of the tissue sample depicted in FIG. 87Aand shows, in yellow (1684), the points that are “masked” by applicationof the [CE]_(spec) metric above. White points (1682) in FIG. 87Bindicate regions that are filtered out by the arbitration and low-signalmask of step 128, while pink points (1686) indicate regions remainingafter application of both the arbitration/low-signal mask of step 128 aswell as the [CE]_(spec) spectral mask.

Step 1446 in FIG. 74 depicts the determination of a fluids/mucus([MU]_(spec)) spectral mask using data from the broadband reflectancespectrum, BB, at each interrogation point of the tissue sample followingthe arbitration and low-signal masking step 128. In one alternateembodiment, the fluorescence spectrum is used in place of or in additionto the broadband reflectance spectrum. The [MU]_(spec) mask identifiestissue sites covered with thick, opaque, and light-colored mucus, aswell as fluid that is pooling in the os or on top of the speculum duringa patient scan.

FIGS. 88, 89, 90, and 91 show steps in an exemplary discriminationanalysis to determine a [MU]_(spec) spectral mask. FIG. 106 shows agraph 1688 depicting as a function of wavelength 1690 the mean broadbandreflectance values 1692 for a set of known pooling fluids regions 1694and a set of known CIN 2/3 tissue regions 1696. FIG. 89 shows a graph1697 depicting as a function of wavelength 1698 the mean fluorescenceintensity values 1700 for the set of known pooling fluids regions 1702and the set of known CIN 2/3 tissue regions 1704. The difference betweencurves 1694 and 1696 in FIG. 88 is pronounced. Thus, in one embodiment,a term is included in the [MU]_(spec) mask metric based on the bestratio of wavelength found to maximize values of D in the discriminationequation, Equation 91, as follows:

$\begin{matrix}{D = \frac{{{\mu\left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Outlier} - {\mu\left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Tissue}}}{\sqrt{{\sigma^{2}\left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Outlier} + {\sigma^{2}\left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Tissue}}}} & (91)\end{matrix}$

In one embodiment, values of D above are maximized using the broadbandreflectance ratio BB(594 nm)/BB(610 nm).

A scatter plot depicting discrimination between pooling fluids regionsand CIN 2/3 tissue regions for a set of known reference data areobtained by comparing the ratio of arbitrated broadband intensity,BB(594 nm)/BB(610 nm) to a threshold constant. Then, a graph of truepositive ratio (TPR) versus false positive ratio (FPR) in thediscrimination between pooling fluids regions and CIN 2/3 tissue regionsare obtained using a threshold constant. For example, a TPR of 56.3% andan FPR of 0.9% is obtained using a threshold constant of 0.74. The ratioof false positives may be reduced by adjusting the threshold.

FIG. 90 shows a graph 1722 depicting as a function of wavelength 1724the mean broadband reflectance values 1726 for a set of known mucusregions 1728 and a set of known CIN 2/3 tissue regions 1730. FIG. 91shows a graph 1732 depicting as a function of wavelength 1734 the meanfluorescence intensity values 1736 for the set of known mucus regions1738 and the set of known CIN 2/3 tissue regions 1740. The differencebetween curves 1728 and 1730 in FIG. 90 is pronounced. Thus, in oneembodiment, a term is included in the [MU]_(spec) metric based on thebest ratio of wavelength found to maximize values of D in thediscrimination equation, Equation 91 above. In one embodiment, thisratio is BB(456 nm)/BB(542 nm).

A scatter plot depicting discrimination between mucus regions and CIN2/3 tissue regions for a set of known reference data may be obtained bycomparing the ratio of arbitrated broadband intensity, BB(456 nm)/BB(542nm) to a threshold constant. Then, a graph of true positive ratio (TPR)1752 versus false positive ratio (FPR) 1754 in the discriminationbetween mucus regions and CIN 2/3 tissue regions are obtained using athreshold constant. For example, a TPR of 30.4% and an FPR of 0.8% isobtained using a threshold constant of 1.06. The ratio of falsepositives may be reduced by adjusting the threshold.

In one embodiment, the discrimination analysis illustrated in FIGS. 88,89, 90, and 91 lead to the overall [MU]_(spec) mask metric as follows:BB(456 nm)/BB(542 nm)<1.06 ORBB(594 nm)/BB(610 nm)>0.74.The metric above combines the sites identified by the pooled fluidsmask, as indicated by the bottom line of the metric above, with thesites identified by the mucus mask, as indicated by the top line of themetric above.

FIG. 92A depicts a reference image 1758 of cervical tissue from apatient scan in which spectral data is used in arbitration and[MU]_(spec) spectral masking. FIG. 92B is an obgram 1770 of theinterrogation points (regions) of the tissue sample depicted in FIG. 92Aand shows, in yellow (1768), the points that are “masked” by applicationof the [MU]_(spec) metric above. White points (1766) in FIG. 92Bindicate regions that are filtered out by the arbitration and initiallow-signal mask of step 128, while pink points (1770) indicate regionsremaining after application of both the arbitration/low-signal mask ofstep 128 as well as the [MU]_(spec) spectral mask.

Image Masking

The invention also comprises an image masking feature. Image masksidentify data from one or more images obtained during patientexamination that are potentially non-representative of regions ofinterest of the tissue sample. Potentially non-representative dataincludes data that are affected by the presence of an obstruction, suchas blood, mucus, a speculum, pooled fluid, or foam, for example. In oneembodiment, a reference image of an in-situ cervical tissue sample isobtained just prior to a spectral scan, and image masks are determinedfrom the reference image to reveal where there may be an obstruction orother area that is not of diagnostic interest. Areas that are not ofdiagnostic interest include regions affected by glare, regions of theos, vaginal wall tissue, or regions that are otherwise outside the areaof interest of the tissue sample. These areas may then be “masked” fromthe analysis of spectral data obtained from tissue regions that coincidewith the obstruction, for example. The image masks are combined witheach other and/or with the spectral masks, as shown in block 1422 ofFIG. 73 and as shown in FIG. 74. The resultant masks include “hard”masks and “soft” masks, described in more detail herein. Hard masksresult in a characterization (or diagnosis) of “Indeterminate” ataffected regions, while soft masking provides a means of weightingspectral data according to the likelihood that the data isrepresentative of clear, unobstructed tissue in a region of interest.

In one embodiment, image masks are combined and applied as indicated inthe block diagram 1438 of FIG. 74, in order to account for theidentification of image-masked interrogation points in thetissue-class/state-of-health classification step 1432. Steps 1448, 1450,1452, 1454, 1456, 1458, 1460, 1462, and 1464 in FIG. 74 depict thedetermination of image masks from the image data obtained around thetime of the patient spectral scan. These image masks are discussed inmore detail below.

FIG. 93 depicts image masks 1782, 1784, 1786 determined from a referenceimage of a tissue sample and conceptually shows how the image masks arecombined with respect to each interrogation point (region) 1790 of thetissue sample, according to one embodiment. Generally, for a giveninterrogation point 1790 in the scan pattern 1788, the system determineswhether any of the features detected by the image masks, such as the osimage mask 1784 and the blood image mask 1786, intersects thatinterrogation point (region) 1790. For certain image masks, a percentcoverage is determined for regions they intersect. For some image masks,if any of the mask intersects a region, the region is flagged as“masked”.

In one embodiment, a backend process determines the coverage of one ormore masks for each interrogation point of the scanning pattern. Given aknown correspondence between image pixels and interrogation points, agiven point is assigned a percentage coverage value for a featuredetermined by a given image mask, such as blood detected by theBlood_(vid) image mask 1458 in FIG. 74. The percentage coverage valuecorresponds to the number of pixels for the given interrogation pointcoinciding with the selected image mask feature, divided by the totalnumber of pixels for that interrogation point. For example, if the bloodmask for a given interrogation point coincides with 12 out of 283 pixelsthat cover the point, then the percentage coverage for thatinterrogation point is 12/283, or 4.2%.

Steps 1468, 1470, 1472, and 1474 in FIG. 74 demonstrate how the imagemasks are combined in one embodiment, and steps 1466, 1476, 1424, 1478,1480, 1424, 1426, 1428, and 1430 in FIG. 74 demonstrate how the combinedmasks are applied with respect to the tissue-class/state-of-healthclassifications at the spectral interrogation points, in one embodiment.These steps are discussed in more detail herein.

The image masks in FIG. 74 are determined using image processingmethods. These methods include color representation, spatial filtering,image thresholding, morphological processing, histogram processing, andcomponent labeling methods, for example.

In one embodiment, images are obtained in 24-bit RGB format. There are anumber of ways to quantify image intensity and other imagecharacteristics at each pixel. Most of the image masks in FIG. 74 usevalues of luminance (grayscale intensity) at each pixel. In oneembodiment, luminance, Y, at a given pixel is defined as follows:Y=0.299R+0.587G+0.114B  (92)where Y is expressed in terms of red (R), green (G), and blue (B)intensities; and where R, G, and B range from 0 to 255 for a 24-bit RGBimage. Some of the image masks in FIG. 74 use one or more of thefollowing quantities:

$\begin{matrix}{{redness} = {\frac{R - G}{R + G} + \frac{R - B}{R + B}}} & (93) \\{{greenness} = {\frac{G - R}{G + R} + \frac{G - B}{G + B}}} & (94) \\{{blueness} = {\frac{B - R}{B + R} + \frac{B - G}{B + G}}} & (95)\end{matrix}$where R, G, and B are as defined above.

Determination of the image masks in FIG. 74 includes the use ofone-dimensional (1-D) and two-dimensional (2-D) filters. The types offilters used includes low-pass, smoothing filters and gradient, edgedetection filters. The 1-D filters generally range in size from 3 to 21pixels and the 2-D filters generally range from 3×3 to 15×35 pixels,although other filter sizes may be used. In one embodiment, box carfilters are the preferred type of low-pass (smoothing) filters. Box carfilters replace the value at the center of the filter support with anequally-weighted average of all pixels within the filter support. In oneembodiment, the preferred types of gradient filters are Sobel andLaplacian of Gaussian filters.

In one embodiment, the image masks in FIG. 74 are determined using imagethresholding, a subclass of image segmentation in which the image isdivided into two segments. The criterion for assigning a pixel to one ofthe two segments is whether its value is less than, larger than, orequal to a prescribed threshold value. A binary image may be obtained bymarking pixels having values less than the threshold with zeros and theremaining pixels with ones. Some image masks are determined usingmultiple thresholding and/or dynamic thresholding, where the thresholdfor each pixel or group of pixels is computed dynamically from imagestatistics, for example.

In one embodiment, the determination of the image masks in FIG. 74includes binary morphological processing. Binary morphologicalprocessing is performed on a binarized (thresholded) image to smoothobject boundaries, change the size of objects, fill holes withinobjects, remove small objects, and/or separate nearby objects.Morphological operators used herein include dilation, erosion, opening,and closing. An operator may be defined by (1) a binary mask orstructuring element, (2) the mask origin, and (3) a mathematicaloperation that defines the value of the origin of the mask. In oneembodiment, a 3×3 square structuring element is used, and is generallypreferred unless otherwise specified.

In one embodiment, dilation increases the size of a binary object byhalf the size of the operator mask/structuring element. Erosion is theinverse of dilation and decreases the size of a binary object. Forexample, an erosion of a binary object is equivalent to the dilation ofthe background (non-objects). Opening is an erosion followed by adilation, and closing is a dilation followed by an erosion. As usedherein, dil(Img, n) denotes performing n dilation steps on image Imgwith a 3×3 square structuring element, and erod(Img, n) denotesperforming n erosion steps on image Img with a 3×3 square structuringelement.

In one embodiment, the determination of the image masks in FIG. 74includes the use of histograms. Here, a histogram relates intervals ofpixel luminance values (or other quantification) to the number of pixelsthat fall within those intervals. In one embodiment, histogramprocessing includes smoothing a histogram using a 1-D low-pass filter,detecting one or more peaks and/or valleys (maxima and minima), and/orcomputing thresholds based on the peaks and/or valleys.

In one embodiment, the determination of the image masks in FIG. 74includes component labeling. Component labeling is used to joinneighboring pixels into connected regions that comprise the components(objects) in an image. Extracting and labeling of various disjoint andconnected components (objects) in an image allows separate analysis foreach object.

In component labeling of a binary image using 8-connectivity, aconnected components labeling operator scans the image by moving alongthe row until coming to a pixel p with a value V=1, then the operatorexamines the four neighbors of p that have already been encountered inthe scan. For example, the four neighbors of p are (1) the pixel to theleft of p, (2) the pixel directly above p, and (3,4) the two pixels inthe row above pixel p that are diagonal to pixel p. Based on thisinformation, p is labeled as follows:

-   -   If all four neighbors have V=0, assign a new label to p, ELSE    -   If only one neighbor has V=0, assign its label to p, ELSE    -   If one or more neighbors have a value of 1, assign one of the        labels to p and note the equivalences.        After completing the scan, the equivalent label pairs are sorted        into equivalence classes and a unique label is assigned to each        class. A second scan is made through the image, and each label        is replaced by the label assigned to its equivalence class.        Component labeling of a binary image with 4-connectivity may be        performed similarly.

In one embodiment, an image mask is determined using data from arepresentative image of a tissue sample obtained near to the time of aspectral scan of the tissue (just before, during, and/or just after thespectral scan). In one embodiment, the representative image is obtainedwithin about 30 seconds of the beginning or ending of the spectral scan;in another embodiment, the representative image is obtained within about1 minute of the beginning or ending of the spectral scan; and in anotherembodiment, the representative image is obtained within about 2 minutesof the beginning or ending of the spectral scan. Other ranges of time inrelation to the spectral scan are possible. In one embodiment, there isonly one reference image from which all the image masks are determined.

Glare_(vid)

Step 1462 in FIG. 74 depicts the determination of a glare mask,Glare_(vid), for an image of a tissue sample. Glare_(vid) indicatesregions of glare in a tissue image. Glare_(vid) is also used in thecomputation of other image masks. FIG. 94A depicts an exemplary image1794 of cervical tissue used to determine a corresponding glare imagemask, Glare_(vid). FIG. 94B represents a binary glare image mask,Glare_(vid), 1796 corresponding to the tissue image 1794 in FIG. 94A.

The white specks of glare in the tissue image 1794 in FIG. 94A areidentified by the image mask 1796. The image mask is determined using anadaptive thresholding image processing procedure. Different thresholdsare applied in different areas of the image, since the amount ofillumination may vary over the image, and a threshold luminanceindicative of glare in one area of the image may not indicate glare inanother, lighter area of the image. In one embodiment, for example, animage of a tissue sample is divided into a 4 by 4 grid of equally-sized,non-overlapping blocks. A suitable glare threshold is computed for eachblock, and the subimage within that block is binarized with the computedthreshold to yield a portion of the output glare segmentation mask,Glare_(vid). Each block computation is independent, and blocks areserially processed until the complete binary glare mask, Glare_(vid), iscompletely calculated. For each block, multiple thresholds based onluminance value and/or histogram shape are computed and are used todetect and process bimodal distributions.

FIG. 95 is a block diagram depicting steps in a method of determining aglare image mask, Glare_(vid), for an image of cervical tissue. Step1802 in FIG. 95 indicates dividing an image into a 4×4 grid of cells(blocks) 1804 and computing a histogram for each cell that is then usedto determine thresholds 1806 applicable to that block. Each histogramcorrelates intervals of luminance values, Y, (Y ranging from 0 to 255)to the number of pixels in the cell (subimage) having luminance valueswithin those intervals. Step 1806 in FIG. 95 indicates determiningthresholds applicable to a given cell of the image. For example, FIG. 96shows a histogram 1842 for one cell of an exemplary image. Curve 1848indicates a raw histogram plot for the cell (subimage), and curve 1850indicates the curve after 1-D filtering using a 21-point box car filter.Quantities 1840 related to thresholding that are calculated from eachhistogram 1842 include T_(pk) (peak), T_(vy) (valley), T_(lp), T_(s),T_(do), and T₉₀, all of which are described below. The exemplaryhistogram 1842 in FIG. 96 shows bars indicating values of T_(pk) (1852),T_(vy) (1854), T_(lp) (1856), T_(s) (1858), T_(do) (1860), and T₉₀(1862) for the cell histogram curve. The heavy dashed line (1854)indicates the final threshold chosen for the cell according to themethod of FIG. 95.

The following describes the steps of the method 1800 shown in FIG. 95,according to one embodiment.

The method 1800 in FIG. 95 comprises calculating intended thresholds instep 1806. Four thresholds are computed to decide whether the block(cell) contains glare:

-   -   1. Ts=mean+3*std where mean is the average intensity of the        block and std its standard deviation.    -   2. Tlp=last peak of smoothed histogram. Smoothing is performed        using a width 5 maximum order statistic filter.    -   3. Tdo=Lmax+2 (Ldo−Lmax) where Lmax is the index (gray level) at        which the 21-point boxcar filtered histogram, sHist, reaches it        maximum value sHistMax, and Ldo is the first point after Lmax at        which the filtered histogram value falls below 0.1*sHistMax.    -   4. T90 is defined so that 90% of the gray levels greater than        210 are greater than T90.

Next, the method 1800 in FIG. 95 includes a block (cell) glare detectorin step 1810. The block (cell) glare detector assesses whether glare ispresent in the block and selects the next block if no glare is detected.The block is assumed to have no glare if the following condition is met:((Tlp<Ts) AND (Ts<T90)) OR((Tlp<Tdo) AND (Tdo<T90)) OR((Tlp<Tdo) AND (Tlp<Ts) AND (Tlp<T90)) OR((Tlp<0.8*T90) AND (no valid glare mode as described in the bimodalhistogram detection section below)).

Next, the method 1800 in FIG. 95 comprises selecting a candidatethreshold, Tc, in step 1812. A candidate threshold Tc is chosen basedupon the values of the intermediate thresholds Ts, Tlp, Tdo and T90according to the following rules:

-   -   1. if (Tlp<T90):        -   a. if (Tdo<Tlp/2):            -   i. if (Ts<Tlp): Tc=(Ts+Tlp)/2            -   ii. else Tc=Tlp        -   b. else Tc=min(Tdo, Tlp)    -   2. (Tlp>=T90) High intensity glare        -   a. if (Ts<=T90):            -   i. if ((Ts<=100) AND (Tdo<=100)): Tc=max(Ts, Tdo)            -   ii. else if ((Ts<=100) and (Tdo>100): Tc=min(Tdo, Tlp)            -   iii. else Tc=min(Ts, Tdo)        -   b. else            -   i. if (Tdo<100): Tc=T90            -   ii. else Tc=min(Tdo, T90).

Next, the method 1800 in FIG. 95 includes detecting a bimodal histogramin step 1806. Step 1806 detects bimodal histograms that are likely tosegment glare from non-glare and uses the 21 point boxcar filteredhistogram sHist to determine Tvy after computing Tpk and Tcross, asdescribed herein. To compute Tpk, sHist is searched backwards from theend until point Tpk where the value is greater than the mean and maximumof its 5 closest right and left neigbors and where Tpk is greater orequal to 10. Tcross is the point after Tpk (in backwards search) wherethe histogram value crosses over the value it has at Tpk. If thehistogram is unimodal, Tpk is equal to Lmax, the gray level where sHistattains its max value, and Tcross is 0. Tvy is the minimum point onsHist between Tpk and Tcross if the following glare condition, calledvalid glare mode, is met:(Tpk>175) AND (Tpk>Lmax) AND(sHist[tPk]<0.6*sHist[Lmax]) AND((Tpk−Tcross>20) OR (Tpk>T90)) AND((Tpk>(mean+(1.5*std))) OR (Tpk>T90)).

Next, the method 1800 in FIG. 95 includes selecting a final threshold insteps 1814, 1816, 1818, 1820, 1822, 1824, and 1826. The final thresholdselected depends on whether the histogram is bimodal or unimodal. For abimodal histogram with a valid glare mode, the final threshold T is Tvyif 175<Tvy<Tc. In all other cases (i.e. for unimodal histograms with acandidate threshold Tc and for bimodal histograms with a valleythreshold Tvy ouside the range 175 to Tc), Tc is chosen as the finalthreshold unless it can be incremented until sHist[Tc]<0.01*Shist[Lmax]or Tc>Tlim under the following two conditions. First, if a value Lexists in the range [Tc,255] where sHist[L]>sHist[Tc], define Lmin to bethe gray value where sHist reaches its minimum in the range [Tc,L].Then, Tc should not be incremented beyond Lmin, and the limit thresholdTLim=Lmin. If L<150, then Tlim=210. Secondly, if L does not exist,Tlim=210.

[ROI]_(vid)

Step 1448 in FIG. 74 depicts the determination of a generalregion-of-interest mask, [ROI]_(vid), for an image of a tissue sample.The general region-of-interest mask determines where there is tissue inan image, and removes the non-tissue background. [ROI]_(vid) is alsoused in the computation of other image masks. FIG. 97A depicts anexemplary image 1894 of cervical tissue used to determine acorresponding region-of-interest mask, [ROI]_(vid), 1896 correspondingto the tissue image 1894 in FIG. 97A. The mask 1896 excludes thenon-tissue pixels in image 1894.

The [ROI]_(vid) mask detects the general areas of the image indicativeof tissue, and is determined by thresholding a pre-processed red channelimage of the tissue and by performing additional processing steps toremove unwanted minor regions from the thresholded image, explained inmore detail below.

FIG. 98 is a block diagram 1900 depicting steps in a method ofdetermining a region-of-interest image mask, [ROI]_(vid), for an imageof cervical tissue. The following describes the steps of the methodshown in FIG. 98 (1900), according to one embodiment.

The method 1900 includes pre-processing in step 1902. First, smooth thered channel image by twice applying a 5×5 box car filter. The filteredimage is sRed. Next, compute a best dynamic threshold for sRed asfollows. Create a foreground binary image of sRed using a threshold of15. Create a glare mask binary image, glareMsk, using glare mask processGlare_(vid) above. Create a valid cervix pixel image, validPix, bybinary AND-ing foreground and glareMsk inverse. Binary erode validPix,evalidPix=erod(validPix, 3). In evalidPix, find the top row containingthe first valid pixel, topR; find the bottom row containing the lastvalid pixel, botR; the middle row is expressed as midR=(topR+botR)/2;then, set all evalidPix pixels above midR to 0. Compute mean, mean, andstandard deviation, stdDev, of sRed on the region defined by evalidPix.The best dynamic threshold is then T=max(10, min(mean−1.5 *stdDev, 80)).Threshold sRed using T in step 1904.

Next, the method 1900 in FIG. 98 includes thresholding sRed using T instep 1904. Then, step 1906 is performing a binary component labelingusing 4-way connectivity. Finally, step 1908 is computing the area ofeach object obtained in the previous step and selecting the largestobject. Flood fill the background of the object selected in the previousstep to fill holes. The result is the [ROI]_(vid) mask.

[ST]_(vid)

Step 1450 in FIG. 74 depicts the determination of a smoke tube mask,[ST]_(vid), for an image of a tissue sample. The smoke tube maskdetermines whether the smoke tube portion of the speculum used in theprocedure is showing in the image of the tissue sample. The smoke tubemask also identifies a portion of tissue lying over the smoke tube(which may also be referred to as “smoke tube” tissue) whose opticalproperties are thereby affected, possibly leading to erroneoustissue-class/state-of-health characterization. FIG. 99A depicts anexemplary image 1932 of cervical tissue used to determine acorresponding smoke tube mask, [ST]_(vid), 1934 shown in FIG. 99B. Thesmoke tube mask is determined in part by isolating the two “prongs”holding the smoke tube tissue. The two prongs are visible in the image1932 of FIG. 99A at reference numbers 1930 and 1931. In some images, theprongs are not visible. However, the smoke tube tissue in these images(without visible prongs) is generally either a blue or blue-green colorwith almost no red component; and the smoke tube in these images isidentified (and removed from consideration) by the generalregion-of-interest image mask, [ROI]_(vid).

FIG. 100 is a block diagram 1938 depicting steps in a method ofdetermining a smoke tube mask, [ST]_(vid), for an image of cervicaltissue. Image 1944 is an exemplary input image for which a correspondingsmoke tube mask 1960 is computed. Image 1944 shows a circle 1945 used insteps 1954 and 1956 of the method in FIG. 100.

The following describes the steps of the method shown in FIG. 100,according to one embodiment.

The method 1938 in FIG. 100 comprises step 1946, pre-processing theimage. Pre-processing includes processing each RGB input channel with a3×3 median filter followed by a 3×3 boxcar filter to reduce noise. Step1946 also includes calculating or retrieving the general ROI mask ROImsk([ROI]_(vid), described above) and the glare mask glareMsk (Glare_(vid),described above), and computing the search image, srcImg, as follows.First, compute the redness image Rn. Set to zero all values in Rn thatare oustide ROImsk. Autoscale the redness image to the [0,1] range.Then, compute srchImg, which will be used at the final stages of thealgorithm to compute a rough correlation to find the best circlelocation. SrchImg is a linear combination of the redness and red images:srchImg=(1−A)*Rn+A*R. The linear weight factor A is in the range [0.2,0.8]. Form validPix=ROImsk AND not(dil(glareMsk, 3). Compute mean,meanR, meanG, meanB of the RGB channels on the region defined byvalidPix. The weight A is initially computed as: A=max(0.5,min((2*meanR)/(meanG+meanB), 1.5)). Remap the value of A into the range[0.2, 0.8], A=0.2+(0.6*(A−0.5)). SrchImg is computed using the A factordetermined above.

Next, the method 1938 in FIG. 100 comprises a prong detector filter instep 1948. The prong detector is applied to the red image, R and to anenhanced red image, RE to produce 2 different prong images that will bearbitrated later. First, calculate the red-enhanced image, RE=R+max(R−G,R−B). Next, set up the prong detector filter. The filter is designed tobe sensitive to smoke-tube prongs and to reject glare, edges and otherfeatures. The filter is a rectangular 35 by 15 separable filter. Thehorizontal filter H is defined by H=[−1.5 −1.5 −1.5 −1.5 −1.5 0 0 0 0 01 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 −1.5 −1.5 −1.5 −1.5 −1.5]. Thevertical filter V is a box car filter of length 15. Next, apply theprong filter to R and RE images yielding Rprong and Reprong. Clipfiltered images to 0 and autoscale to the range [0, 1]. Set the bottomhalf of each filtered image as well as the first 20 and the last 20columns to 0 (there are no prongs in these sections of images). Then,find a maximum value for each of the first 125 rows of the 2 filteredimages. Find the constant Rfact and REfact for each filtered image.These constants are defined as the mean of the maxima of the first 125rows divided by mean of the first 125 rows. If (Rfact>Refact) use Rprongas the prong search image, iProng, otherwise use REprong.

Next, the method 1938 in FIG. 100 comprises thresholding, componentanalysis, and prong selection in step 1950. Step 1950 is used to selectprongs. First, threshold iProng image with a threshold of 0.2. Performbinary component labelling to obtain all regions (objects). Computeregions (objects) statistics, including area, centroid, and major andminor axis length. Filter prong regions (objects). Discard each region(object) that statisfies any of the following criteria:

-   -   1. Region size<300.    -   2. iProng maximum on object<0.4.    -   3. Region does not extend above row 100.    -   4. Minor axis length>=30.    -   5. Region does not overlap with ROImsk.    -   6. Region centroid is below row 140.    -   7. Centroid y-value>40 and object thinness=(major axis        length/minor axis length)<=2.        Choose as the main prong the brightest remaining region (i.e        where the region maximum value is greater than the maxima from        all other remaining regions). Filter all other prong regions        based upon the distance from the main prong by calculating the        distance from each region's centroid to the centroid of the main        prong, and discarding the region if the intra-centroid        distance >160 or if the intra-centroid distance <110.

Next, method 1938 in FIG. 100 comprises validation of the selectedprongs in step 1952. For each retained prong object in step 1950, thefollowing computations are peformed to validate the selected prongs.Define pad, the rough distance from an object to its perimeter. Here,pad is set to 8. Form images of the dimension of the bounding box of theobject plus pad pixels on each side (OBJ). Crop the original object ofthe prong search image IOrig, from the original unsmoothed red channelimage, Rorig, and form the binarized image BWProng. Compute internalregion, intReg=erod(dil (OBJ, 2), 1). Compute object perimeter region,perObj=dil((dil(OBJ, 2) AND not (OBJ)), 2). Compute mean and standarddeviation, mean and std, of the object on the interior region, intReg,and the mean, pmean, on the perimeter region perObj. Compute left/rightbias by computing locations of the center points of the object top rowand bottom row, drawing a line connecting those 2 points to divide theperimeter region, perObj, into 2 sections, calculating the mean value ofiProng on each of the half perimeter sections, LperMean, RperMean, andusing the means to compute left/right biases, LRBias usingLRBias=max(LperMean/RperMean, RperMean/LperMean). Discard any objectswhere the following holds: (mean/pmean<1.4) OR (std>0.55) OR((LRBias>1.45) AND (mean/pmean<1.48)). If more than 2 prong candidatesare remaining, keep the one closest to the main prong. If no prongcandidates are left, the smoke tube mask is empty.

Next, method 1938 in FIG. 100 comprises template searching using circlesin step 1954. Step 1954 is used to qualify regions as smoke tubecandidates. First, construct a binary mask, validCervix, of valid cervixpixel locations: by Computing or retrieving blood mask, bloodMsk,(Blood_(vid), described below); Computing or retrieving glare mask,glareMsk, (Glare_(vid), described above), then compute the bloodMskusing validCervix=ROImsk AND not(BWProng) AND not(dil(glareMsk, 3)) ANDnot(bloodMsk). Then, determine an x-coordinate value for the center,xCent, of the circle and radius, rad. For 2 prongs xCent is the halfpoint between centroids of 2 prongs and rad is the half distance bewteenprongs+5. For 1 prong, choose a default rad of 85 and do a left-rightsearch to decide wether the smoke tube is on the left or right. Thex-coordinate values, xCent, for each of the 2 search circles is thex-coordinate of the prong centroid+/−rad. The y-coordinate, yCent, isthe y-coordinate of the prong centroid. For each circle center (xCent,yCent), find all points within rad that are in validCervix and computethe regional mean from the redness image. Then, find all points outsiderad that are in validCervix and compute the regional mean from theredness image. Compute the contrast as the ratio of inner mean rednessto outer mean redness and select the circle with minimum contrast.Discard the previous circle if xCent is within rad/4 from the left orright edge of the image, since it cannot be a smoke tube. Then, use thesearch image, srchImg, to perform an up-down search on the y-coordinate,yCent, to determine the actual smoke tube location using thex-coordinate xCent computed named above in section 2. Repeat the searchwith the redness image Rn if the results are unsatisfactory. A minimumand maximum value for yCent, yCentMin and yCentMax are chosen asfollows:

-   -   1. yCentMin=−rad+yProngBot; where yProngBot is the mean of the        bottom-most points of the prong(s), or the bottom-most point for        a single prong.    -   2. For two prongs, yCentMax=yProngBot−(0.75*rad) i.e. the circle        cannot extend beyond ¼ rad below the bottom of the prongs.    -   3. For one prong, yCentMax=min(yProngBot+rad/3, 150) i.e. the        circle can go quite past the end of the prong, but not below the        150th row of the image.        Three more points spaced (yCentMax−yCentMin)/4 apart are        computed between yCentMax and yCentMin. The search algorithm        uses a total of yCent candidate points. For each yCent        candidate, the inner/outer contrast for circles centered at        (xCent, yCent) are computed using srchImg as follows:    -   1. Find all points within rad that are in validCervix and        compute the regional mean from srchImg.    -   2. Find all points outside rad that are in validCervix and        compute the regional mean from srchImg.    -   3. Compute the contrast as the ratio of the inner mean value of        srchImg to the outer mean value of srchImg and select the circle        with minimum contrast.        Check to see that at least one of the 5 contrast numbers is less        than 1. If not, break out of the loop and proceed no further        with this search. If at least one circle has contrast less than        1, choose the minimum and select a new set of five points        centered around this one using the following steps:    -   1. If the top or bottom point was the minimum, choose that        point, the one below/above it, and three points evenly spaced in        between them.    -   2. If one of the three central points was the minimum, choose        that point with the ones immediately below and above it, and two        additional ones centered in the two spaces that divide those        three.        Using the new set of five points, go back to the computation of        the inner/outer contrast for circles using srchImg, discussed        herein above, and proceed in this way until the distance between        the five points is less than 3 pixels. When the distance between        the points is less than 2 pixels, exit the loop and choose the        yCent with the current minimum contrast number as the final        value of yCent for the circle. The contrast for the final circle        must be less than 0.92 in order for the algorithm to find a        valid circle. If that is not the case, then the search algorithm        is repeated with the pure redness image, Rn instead of srchImg,        which was a mixture of R and Rn. If the Rn search produces an        acceptable result with contrast less than 0.92, then this value        of yCent is used and we can proceed. Otherwise, there is no        suitable circle and the segmentation mask will contain prongs        but no circle.

Finally, method 1938 in FIG. 100 comprises producing the final smoketube segmentation mask in step 1958. First, set the values of all pixelsabove the horizontal line inside the circle which is bisected by thecenter to 1. This effectively casts a “shadow” straight upward from thebottom of the image, and creates the effect that the smoke tube iscoming straight down from outside of the image. The shadowed circle andprong images are combined to yield the final segmentation mask. Clean upany stray non-prongs by performing a flood-fill of “on” valued regionswith seeds in the first or thirtieth row of the image to select onlyobjects that touch the first or thirtieth row of the image.

Os_(vid)

Step 1460 in FIG. 74 depicts the determination of an os image mask,Os_(vid), for an image of a tissue sample. The optical properties of theos region may differ from optical properties of the surrounding tissue.In the method 1438 of FIG. 74, the os image mask is used in soft maskingto penalize data from interrogation points that intersect or lieentirely within the os region. FIG. 101A depicts an exemplary image 1964of cervical tissue used to determine a corresponding os image mask,Os_(vid), 1968, shown in FIG. 101B.

The Os_(vid) image mask is determined using a combination of thresholdsfrom different color channels and using a binary component analysisscheme. An initial mask is formulated from a logical combination ofmasks computed from each color channel, R, G, B, and luminance, Y(equation 94). The four individual masks are computed using athresholding method in which the threshold is set relative to thestatistics of the colorplane values on the image region-of-interest(ROI). A component analysis scheme uses the initial mask to detect an oscandidate area (object), which is validated.

FIG. 102 is a block diagram 1988 depicting steps in a method ofdetermining an os mask, Os_(vid), for an image of cervical tissue. Image1990 is an exemplary input image for which a corresponding os mask 2004is computed. The following describes the steps of the method 1988 shownin FIG. 102, according to one embodiment.

The method 1988 in FIG. 102 includes image preprocessing in step 1992.Preprocessing includes computing luminance Y from RGB componentsY=0.299*R+0.587*G+0.114*B; smoothing RGB channels using 2 iterations ofa 3×3 box car filter; and computing a ROI mask, ROImsk, ([ROI]_(vid))using the method described herein above. Next, process the ROI mask byeroding ROImsk 14 times to obtain eROImsk=erod(ROImsk, 14). Computeannulus perimeter, annMsk: annMsk dil ((eROImsk AND not erod(eROImsk,1)), 4). This is a thick closed binary image which traces the edge ofthe ROI, useful in closing the boundary around any os which might extendto the background. Remove glare in ROImsk by logically AND-ing ROImskwith the complement of the glare mask (obtain as described above) toobtain centerROImsk. Then, compute a mean and standard deviation of eachcolor channel (meanR, stdR, meanG, stdG, meanB, stdB, meanY, stdY) inthe region specified by the centerROImsk.

Next, the method 1988 in FIG. 102 includes thresholding to produce aninitial segmentation mask in step 1994. First, cut-off centerROImskaround the annulus: centerROImsk=centerROImsk AND not (annMsk). Next,form a binary mask for each of the RGBY channels that represents pixelsthat exist in centerROImsk and that satisfy the following conditions:

-   -   1. mskR=(R pixels such that R<(meanR−0.0.40*stdR));    -   2. mskG=(G pixels such that G<(meanG−0.0.65*stdG));    -   3. mskB=(B pixels such that B<(meanB−0.0.75*stdB));    -   4. mskY=(Y pixels such that Y<(meanY−0.0.75*stdY)).        The resulting “initial” segmentation mask, msk, is then defined        by:

-   msk=centerROImsk AND mskR AND mskG AND mskB AND mskY.

Next, the method 1988 in FIG. 102 includes performing a binary componentanalysis in step 1996. This step breaks up the segmentation mask intomultiple objects. First, perform binary component labeling onsegmentation msk. Remove all objects with size less than 125. Breakapart all objects with size greater than 10000. For each object greaterthan 10000 (thisObjMsk), do the following:

-   -   1. Compute mean value meanR and meanY for the area selected by        thisObjMsk in the red and luminance channels.    -   2. Set a new threshold for red and Y as follows:        -   a. redT=0.90*meanR        -   b. lumT=meanY    -   3. Break the object apart, or make it smaller to yield newObj,        then complement thisObjMsk with the region that is not part of        the newly broken-up region:        -   newObj=thisObjMsk AND (R pixels such as R>=redT) AND (Y            pixels such as Y>=IumT).        -   thisObjMsk=thisOBjMsk AND (not(newObj).    -   4. Keep track of the original large image mask (thisObjMsk) that        produces the smaller objects in step c. Create a large object        mask IgObMsk for each thisObjMsk that is set to on for each        large object which was found.

Next, the method 1988 in FIG. 102 includes performing dilation, binarycomponent analysis, and candidate selection in step 1998. Step 1998 isperformed to find os candidates from the multiple binary objectsproduced in step 1996. First, dilate segMsk produced in step 1996 twiceto obtain bMsk=dil(segMsk, 2). Perform a component labeling on bMsk.Discard objects of size less than 125 or greater than 23,000. For eachremaining object, thisObjMsk, apply the following procedure to selectcandidates:

-   -   1. Compute mean, intMeanR, intMeanY, and standard deviation,        intStdR, intStdY for red and luminance channel pixel values in        thisObjMsk.    -   2. Dilate thisObjMsk 7 times to yield dThisObjMsk=dil        (thisObjMsk, 7).    -   3. Compute perimeter mask:        -   a. thisObjPerim=dil((thisObjMsk AND not(erod (dThisObjMsk,            1))), 3).    -   4. Compute mean, perMeanR, perMeanY, and standard deviation,        perStdR, perStdY, for red and luminance channel pixel values in        thisObjPerim.    -   5. Compute the following indicators:        -   a. os brightness (osBright)=intMeanY/perMeanY.        -   b. Perimeter uniformity (perUnif)=perStdR/intStdR.    -   6. An object is an os candidate if:        -   ((osBright<0.85) AND (perUnif<1.75)) OR        -   ((osBright<0.7) AND (perUnif<2.85) AND (part of object came            from large object as recorded in IgObjMsk).

Next, the method 1988 in FIG. 102 includes performing candidatefiltering and final selection in step 2000. The remaining os candidatesare processed as follows. First, discard large non-os objects at theperiphery of the cervix using the following algorithm:

-   -   1. Define a binary image with a centered circular area of radius        150.    -   2. Discard the object if more than half of it is outside the        circle and if perUnif>0.9. This step is done by performing a        logical AND of the object with the circular mask, counting        pixels and comparing to the original size of object.        If the number of remaining objects is greater than 1, perform        the following loop for each object:    -   1. Compute the centroid of the object, and compute the distance        to the image center    -   2. Exit if either:        -   a. The distance to the center is less than 100 for all            objects.        -   b. No object lies within 100 pixels of center and a single            object remains.            Discard the object with the highest perUnif, and go back to            step b. Finally, step 2002 of the method 1988 in FIG. 102            determines the final os mask by twice eroding the final mask            obtained in step 2000.

Blood_(vid)

Step 1458 in FIG. 74 depicts the determination of a blood image mask,Blood_(vid), for an image of a tissue sample. The presence of blood mayadversely affect the optical properties of the underlying tissue. In themethod of FIG. 74, the blood image mask is used in soft masking topenalize data from interrogation points that intersect or lie entirelywithin the blood regions. FIG. 103A depicts an exemplary image 2008 ofcervical tissue used to determine corresponding blood image mask,Blood_(vid), 2012, shown in FIG. 103B.

In one embodiment, the Blood_(vid) image mask is similar to the Os_(vid)image mask in that it is determined using an initial mask formulatedfrom a logical combination of masks computed from each color channel R,G, B and luminance, Y. However, the initial Blood_(vid) image mask isformed as a logical “OR” (not “AND”) combination of the four differentmasks, each designed to capture blood with different colorcharacteristics. Blood may be almost entirely red, in which case the Redchannel is nearly saturated and the green and blue channels are nearlyzero. In other cases, blood is almost completely black and devoid ofcolor. In still other cases, there is a mix of color where the redchannel dominates over green and blue. In one embodiment, theBlood_(vid) mask identifies relatively large regions of blood, not inscattered isolated pixels that may be blood. The logical OR allowscombination of regions of different color characteristics into larger,more significant areas that represent blood. As with the Os_(vid) mask,the Blood_(vid) mask is formulated by thresholding the initial mask andby performing component analysis.

FIG. 104 is a block diagram 2032 depicting steps in a method ofdetermining a blood image mask, Blood_(vid), for an image of cervicaltissue. The following describes the steps of the method 2032 shown inFIG. 104, according to one embodiment.

The method 2032 in FIG. 104 includes image preprocessing in step 2034.Peprocessing includes computing luminance Y from RGB componentsY=0.299*R+0.587*G+0.114*B, and computing the ROI mask, ROImsk,([ROI]_(vid)) using the method described hereinabove.

Next, the method 2032 in FIG. 104 includes mask formation viathresholding in step 2036. The following steps are used to produce aninitial segmentation mask. First, four preliminary masks are generatedto detect “likely” regions of blood, as follows:

-   -   1. To catch blood which is almost completely red, mskA        -   mskA=ROImsk AND (B pixels such as B<15) AND (G pixels such            as G<15) AND (R pixels such as R>2*max(G,B)).    -   2. To catch areas where red dominates over green and blue, mskB:        -   mskB=ROImsk AND (R pixels such as R>G*3) AND (R pixels such            as R>B*3).    -   3. To catch really dark, almost black blood, mskC:        -   mskC=ROImsk AND (R, G, B pixels such as R+G+B<60).    -   4. To catch dark, but not completely black blood, mskD:        -   mskD=ROImsk AND (R, G, B pixels such as R+G+B<150) AND (R            pixels such as R<100) AND (R pixels such as R>max(G,            B)*1.6).            The final candidate segmentation mask, mskOrig, is computed            as follows: mskOrig=mskA OR mskB OR mskC OR mskD.

Next, the method 2032 in FIG. 104 includes object selection using doublethresholding in step 2040. The following steps are used to selectregions that are blood candidate regions. First, a seed mask, seedMsk,is made by eroding mskOrig twice. Then, to connect neighboring pixels,dilate mskOrig once, then erode the result once to obtain cIMskOrig.Finally, to eliminate spurious pixels and regions that are not connectedto larger features, compute mask, msk, by performing a flood fill of“on” valued regions of cIMskOrig with seeds in seedMsk.

Next, the method 2032 in FIG. 104 includes binary component analysis andobject filtering in step 2042. Binary component labeling is performed onmsk to select blood regions. For each labeled object the following stepsare performed:

-   -   1. The Object mask is set to 0. Upon validation, the object mask        is turned ON.    -   2. An interior object is found by shrinking it once (1 erosion        step) unless it disappears, in which case the algorithm reverts        to the original object prior to erosion.    -   3. Dilate the object OBJ 5 times, compute its perimeter and        dilate the perimeter 5 times:        -   ObjPer=dil((OBJ AND not(erod(dil(OBJ,5), 1))), 3).    -   4. For both the interior and perimeter objects, the mean and        standard deviation is found for the Red, Green, and Blue        color-planes within the objects. The interior and perimeter mean        luminance is found as the average of the Red, Green and Blue        means.    -   5. Two indicators are calculated which will help in the decision        step:        -   a. DarkBloodIndicator=(Perimeter Red mean)/(Interior Red            mean). This number is high for dark or black blood because            there is more red in the perimeter than in the interior.        -   b. BrightBloodIndicator=((Perimeter Green Mean+Perimeter            Blue Bean)/Perimeter Red Mean)/((Interior Green            Mean+Interior Blue Bean)/Interior Red Mean). This number is            large when the interior region has a much higher red content            than green and blue as compared to the perimeter.    -   6. If the following three conditions are met, the region is        considered to be a “noisy” feature which is most likely near the        edge of the cervix. This determination affects the decision        rules to follow:        -   a. Interior mean Red<40        -   b. (Interior standard deviation of Red>Interior mean Red) OR            (Interior standard deviation of Green>Interior mean Green)            OR (Interior standard deviation of Blue>Interior mean Blue)        -   c. DarkBloodindicator<5.    -   7. The decision rules: If any of the following three rules are        satisfied, then this object is Blood. Otherwise it is not.        -   a. DarkBloodindicator>2.5 AND not “noisy”;        -   b. BrightBloodIndicator>2.25 AND not “noisy”;        -   c. BrightBloodindicator>2.25 AND DarkBloodindicator>2.5 (in            this case it doesn't matter if it's a “noisy”).    -   8. If the object is blood, it is turned ON in the final        segmentation mask.

Finally, the method 2032 in FIG. 104 includes determining the finalblood mask in step 2044. Step 2044 includes performing a flood-fill ofall objects in which the seed objects were found to be blood. Thisyields the final blood segmentation.

Mucus_(vid)

Step 1464 in FIG. 74 depicts the determination of a mucus image mask,Mucus_(vid), for an image of a tissue sample. The presence of mucus mayaffect the optical properties of the underlying tissue, possibly causingthe tissue-class/state-of-health characterization in those regions to beerroneous. In the method 1438 of FIG. 74, the mucus mask is used in softmasking to penalize data from interrogation points that intersect or lieentirely within the mucus regions. FIG. 105A depicts an exemplary image2064 of cervical tissue used to determine a corresponding mucus imagemask, Mucus_(vid), 2068 shown in FIG. 105B.

In one embodiment, the Mucus_(vid) image mask is a modified blood imagemask, tuned to search for greenish or bright bluish objects. FIG. 106 isa block diagram 2072 depicting steps in a method of determining a mucusmask, Mucus_(vid), for an image of cervical tissue. The followingdescribes steps of the method 2072 shown in FIG. 106, according to oneembodiment.

The method 2072 in FIG. 106 includes preprocessing in step 2074.Preprocessing includes processing each RGB input channel with a 3×3median filter followed by a 3×3 boxcar filter to reduce noise. Then,calculate or retrieve the following masks:

-   -   1. Glare mask (Glare_(vid)): dilate glare mask once to yield        glareMsk    -   2. ROI mask ([ROI]_(vid)): ROImsk    -   3. Blood mask (Blood_(vid)): bloodMsk    -   4. os mask (Os_(vid)): osMsk        Compute a valid cervix pixels mask, validCervix, by AND-ing the        ROImsk with the complement of the other masks as follows:        validCervix=ROImsk AND not(glareMsk) AND not(bloodMsk) AND        not(osMsk).

Next, the method 2072 in FIG. 106 includes mask formation viathresholding and morphological processing in step 2076. The followingsteps are used to produce an initial mucus segmentation mask. First,calculate the means, meanR, meanG and meanB, for the RGB channels on thevalidCervix region. Compute the difference, RGgap between the red andgreen mean: RGgap=meanR−meanG. Create a binary mask, mskOrig, accordingto the following rule: mskorig=ROImsk AND (R, G, B pixels such as((2*G−R−B)>=(10−RGgap/3))). This rule selects regions where green issomewhat higher than either red or blue relative to the gap. Finally,process the binary mask with an opening morphological operator to obtainopMsk, as follows:

-   -   1. Perform two erosions with a 3-by-3 disk structuring element.    -   2. Perform one dilation with a 3-by-3 square structuring        element.    -   3. Perform one dilation with a 3-by-3 disk structuring element.

Next, the method 2072 in FIG. 106 includes object selection using doublethresholding in step 2080. The followings steps are used to selectobjects from the initial segmentation mask by computation of seedpoints. First, a seed image, seedMsk, is computed by eroding opMsk 3times. Then, opMsk is dilated twice then eroded once. Objects in opMskare selected using seedMsk. For example, object I is selected at pointswhere opMsk and seedMsk intersect, then selMsk is defined as theresulting object selection mask.

Then, the method 2072 in FIG. 106 includes binary component analysis andobject filtering in step 2082. The following steps are applied to allobjects selected in step 2080:

-   -   1. Perform binary component labelling on all selected objects in        selMsk.    -   2. Set final segmentation mask to all 0's.    -   3. Compute area for each object in selMsk and discard any object        with an area less than 1000 pixels, update selMsk by removing        discarded objects    -   4. Process all remaining objects in selMsk as follows (steps        2084, 2086):        -   a. Compute mean and standard deviations of the red, green            and blue smoothed images, meanR, meanG, meanB, stdR, stdG,            stdB, for each object.        -   b. Compute the object perimeter for each object:            -   i. Binary object, binObj, is dilated 15 times                dilBinObj=dil(binObj, 15).            -   ii. Object perimeter is computed and then dilated:                perBinObj=dil((dilBinObj AND not(erod(dilBinObj, 1)),                4).        -   c. Compute mean and standard deviations on each color            channel, pmeanR, pmeanG, pmeanB, pstdR, pstdG, pstdB for            each region's perimeter.        -   d. Compute six decision rule indicators:            -   i. Mucus Indicator 1:                mucInd1=(meanG/pmeanG)*(pmeanR/meanR)            -   ii. Mucus Indicator 2:                -   mucInd2=(meanG/pmeanG)*(pmeanR/meanR)*(meanB/pmeanB)            -   iii. Green bright indicator:                gBrightInd=3*meanG−meanR−meanB            -   iv. Local variation quotient:                locVarQuo=(stdR+stdG+stdB)/(psdfr+pstdG+pstdB)            -   v. Target laser Indicator:                targLasInd=(meanG*(pmeanR+pmeanB))/(pmeanG*(meanR+meanB))            -   vi. Blue not too bright indicator: bNotBrightInd if                ((meanB>meanR) AND (meanB>meanG))                bNotBrightInd=(meanG−meanR)/(2*abs(meanB−meanG)            -   else bNotBrightInd=10.        -   e. Object is not mucus object if the following holds:            (mucInd1<1.25) OR (mucInd2<1.5) OR (gBrightInd<100) OR            (bNotBrighInd<1) OR (targLasInd>1.5) OR (locVarQuo>1.75).        -   f. If the object is selected as a mucus object, it is added            to the final mucus mask.

[SP]_(vid)

Step 1452 in FIG. 74 depicts the determination of a speculum image mask,[SP]_(vid), for an image of a tissue sample. [SP]_(vid) is used in hardmasking in the tissue characterization method 1438 of FIG. 74. Here,data from the interrogation points that intersect the speculum areremoved from consideration in the tissue-class/state-of-healthclassification steps. FIG. 107A depicts an exemplary image, 2098, ofcervical tissue used to determine the corresponding speculum image mask,[SP]_(vid), 2100, shown in FIG. 107B.

In one embodiment, the speculum image mask is determined by findingcircles near the bottom of the image. Projections of a number ofdifferent types of speculums resemble circles of different radii. In oneembodiment, two types of circle searches are used: an outer bottomsearch and an inner bottom search. The outer bottom search finds pointsnear the bottom edge of the general region-of-interest and inferscircles from these points. If multiple circles result, they areevaluated to find the one that best models the curvature at the bottomof the region-of-interest. A circle that models this curvature wellenough is used to form the speculum segmentation mask, [SP]_(vid).

If the outer bottom search does not produce a circle that models the ROIcurvature well enough, then another search is performed to find a circlethat models the curvature of a speculum within the ROI. This is theinner bottom search, and may be necessary where there is significantreflection of light from the speculum. In the inner bottom search, a setof angular projections is formed based on a best guess of the center ofcurvature from the outer circle search. The projections are thenanalyzed to find a significant intensity trough near the end of theprojections that agrees with the general expected location of a speculumat the bottom of the image. The projection analysis provides new pointswith which to model circles, and the resulting circles are evaluatedusing the image data to detect the presence of a speculum.

FIG. 108 is a block diagram 2112 depicting steps in a method ofdetermining a speculum image mask, [SP]_(vid), for an image of cervicaltissue. The following describes the steps of the method 2112 shown inFIG. 108, according to one embodiment.

The method 2112 in FIG. 108 includes image preprocessing in steps 2114and 2116. The following steps are used to preprocess the image used inspeculum mask computation. First, remove glare from the RGB image byperforming the following:

-   -   1. Calculate or retrieve glare mask, glareMsk (Glare_(vid)).    -   2. Dilate glareMsk 4 times to obtain dilGlareMsk.    -   3. Filter the RGB values using dilGlareMsk to perform run-length        boundary interpolation as follows:        -   a. Raster scan each row of dilGlareMsk to find all            beginnings and ends of pixel runs.        -   b. For each pixel P(x,y) in a given run specified by            beginning point P(xb, y) and end point P(xe,y) in the            intensity image, replace P(x,y) by half the linearly            interpolated value at P(x,y) from P(xb,y) and P(xe,y).        -   c. Raster scan each column of dilGlareMsk to find all            beginnings and ends of pixel runs.        -   d. For each pixel P(x,y) in a given run specified by            beginning point P(x,yb) and end point P(x,ye) in the            intensity image, add to P(x,y) half the linearly            interpolated value at P(x,y) from P(x,yb) and P(x,ye).            Then, smooth the RGB channels by filtering twice with a 5×5            box car filter. Finally, calculate or retrieve the ROI mask,            ROImsk ([ROI]_(vid)). Next, the method 2112 in FIG. 108            includes outer bottom circle detection in step 2120. The            outer bottom circle detection is designed to find the best            circular segmentation matching the bottom of ROImsk. Step            2120 includes the following:    -   1. Where width specifies the image width, compute the x-location        of 7 columns (defined by none the intervals C_(i)=i·width/10,        where i=1 to 9). The x-locations are used to determine y-values.        The resultant (x,y) pairs are used to find different candidate        circles.    -   2. Four candidate circles—narrow, wide, left, and right—are        calculated from the x values using the following matrix:        -   a. Narrow circle: C3 C5 C7        -   b. Wide circle: C2 C5 C8        -   c. Left circle: C2 C4 C6        -   d. Right circle: C4 C6 C8    -   3. The y-values are determined by scanning the y-axis, at a        given x-position, starting at the bottom, until an “on” pixel is        encountered in ROImsk. The same process is performed for 5        adjacent pixels to the right and left of the given x-position.        The resulting 11 y-values are averaged to obtain the y-value        used for calculating circles at the given x-position.    -   4. For each set of x values defined by the rows in the matrix        above, the y values are computed as described above, and the        resulting three pairs of coordinates are used to determine a        unique circle intersecting these 3 points.    -   5. A candidate circle is retained if:        -   a. Radius R>250 AND        -   b. R<700 AND        -   c. The circle's center lies at a y value less than 240 (half            the image height).

Next, the method 2112 in FIG. 108 includes validation of the outercircle in step 2122. The following steps are used to validate the outercircle:

-   -   1. If circles remain after the previous pruning, perform the        following evaluation procedure:        -   a. Compute candidate circle center, draw perimeter at given            radius and construct 2 offset regions from the drawn            perimeter.        -   b. The average intensity values, meanTop and meanBot, are            calculated for each region on the red image.        -   c. The BotTopRatio is calculated as the ratio of meanTop to            meanBot.            -   i. The top region is centered 10 pixels above the                perimeter of the circle, and is 7 pixels in height. For                example, for a given (x0,y0) point on the perimeter, the                vertical region at x0 comprises the pixels in the range                (x0, y0+10) to (x0, y0+10−7).            -   ii. Similarly, the bottom region is centered 10 pixels                below the perimeter of the circle, and is 7 pixels in                height.        -   d. The average intensity values, meanTop and meanBot, are            calculated for each region on the red image.        -   e. The BotTopRatio is calculated as the ratio of meanTop to            meanBot.    -   2. The circle with the best fit to the actual speculum should        minimize this ratio. If there is more than one circle remaining,        the circle with minimum BotTopRatio is chosen.    -   3. If BotTopRatio>0.55, the circle is rejected, and it is        concluded that the outer bottom circle detection found no valid        circle.        If BotTopRatio<0.55, the circle is kept as the initial result        for the speculum segmentation. If the outer circle detection        produces a circle with a strong enough representation of the        speculum, then this is taken as the result and an inner speculum        search is not done. Otherwise the inner speculum search is done.        If no circle is found using the outer algorithm, perform the        inner bottom speculum search. If the outer search finds a        circle, look at the BotTopRatio to determine whether it        qualifies:    -   1. If BotTopRatio<0.275, take the outer circle as the final        segmentation mask and stop.    -   2. If BotTopRatio>=0.275, try the inner speculum search to see        if it yields a satisfactory result.

Next, the method 2112 in FIG. 108 includes inner bottom circle detectionin step 2126. The Inner bottom circle detection algorithm looks forcircles within the ROI mask by calculating angular projections andlooking for “valleys” in the projections to determine points that can beused to infer circles. The resulting circles are evaluated with a schemesimilar to the one for outer bottom circle detection. Step 2126 includesthe following:

-   -   1. Angular projection center point selection:        -   a. If an outer circle was detected, use the center point of            the outer circle.        -   b. Else, use the point (n/2,1), where n is the width of the            image.    -   2. The inner speculum search is done on the red color-plane R        and a redness-enhanced red image ERn. The search results from        the two images R and ERn are evaluated as a group and the best        result is taken from the entire set. The redness enhanced red        image is given by ERn=(2*R+Rn)/3, where Rn is the redness image        defined in Equation 95. If no inner speculum is found from the        redness enhanced red image, then the inner speculum search has        determined that there is no identifiable inner speculum. The        inner speculum search algorithm is described in the subsequent        steps.    -   3. Calculate angular projections as follows:        -   a. Five x-values give the center of each projection as it            crosses the bottom row of the image: [C0 C2 C4 C6 C8].        -   b. From these x-values, the angle thetaCtr, the central            angle for the projection, is computed.        -   c. For each angle thetaCtr, a projection sweeping out 10            degrees (5 degrees on each side of thetaCtr) is calculated.        -   d. For each 10 degree span, 50 equidistant line profiles            (10/50 degrees) are used to calculate the projection. The            profiles extend from the center point to the point where the            line at each angle crosses the bottom row of the image.        -   e. The 50 profiles are averaged to yield the projection for            each of the angles thetaCtr.        -   f. Each projection profile is filtered with a 15 sample long            boxcar moving window averager.    -   4. Each projection is searched backward to find the first “peak”        in the projection, then search backwards again until the valley        beyond that peak is found. This valley usually occurs near the        boundary between the speculum and the cervix. Not every        projection will yield a good valley point V. The criteria for        finding the valley V of a projection P are as follows:        -   a. P(V)<=mean(P(V+k) for all k in [1:12] (12 samples after            V);        -   b. P(V)<=mean(P(V+k) for all k in [−12:−1](12 samples before            V);        -   c. P(V)<=P (V+k) for all k in [−12:12];        -   d. P(V)<P (V+k)−4 for some k in [V:length(P)] (peak-valley            is >=4);        -   e. For valley V, find the y coordinate value y_(V) and check            that y_(V)>300.    -   5. After V is located, search backwards to find the point VMin        where the first derivative of the projection is less than        K*minSlope, where minSlope is the minimum slope between the        valley V and the maximum of P(n) for n in [1:V], and K is a        constant parameter set to 0.3. VMin becomes the final point used        for inferring circles from this projection.    -   6. If the number of points to infer circles (calculated from the        valleys as described above) is greater than 3, then as many        circles as possible can be identified from these points and        evaluated. The circles are chosen from the following matrix:        -   CircleIDX=[1 3 5; % X−X−X            -   2 3 4; % −X X X−            -   1 2 3; % X X X−−            -   3 4 5; % −−X X X            -   1 2 4; % X X−X −            -   2 4 5; % −X−X X            -   1 3 4; % X−X X −            -   2 3 5; % −X X−X            -   1 2 5; % X X−−X            -   1 4 5]; % X−−X X        -   where the elements of the matrix correspond to the five            projections computed above. If a specific projection j fails            to yield an acceptable valley point, then all rows of the            CircleIDX matrix which contain j are removed.    -   7. All remaining rows in CircleIDX are used to select        projections for inferring circles. The circles are calculated by        first getting (x, y) coordinates for the 3 points defined in the        steps above, using the center of projection and the radius along        the projection. A unique circle is fitted through the 3 points,        unless points are collinear, and circle center (xCent, yCent)        and radius rad are computed.

Next, the method 2112 in FIG. 108 includes validation of the innerbottom circle in step 2128. The following steps are used to validate theinner bottom circle:

-   -   1. For each circle, the circle is discarded if any of the        following conditions applies:        -   a. rad<250 (the circle is too small to be a speculum)        -   b. yCent>(image height)/2 (center of circle in lower half of            image or beyond).    -   2. Each remaining circle is evaluated with the following        technique:        -   a. A temporary image is defined for identifying three            different regions specific to the circle. It is an 8-bit            image with the following values:            -   i. 1 for the “inner” region, which is the region between                the circle and another circle whose center is 12 pixels                below the original one.            -   ii. 2 for the “bottom” region, which is a 12 pixel wide                circle drawn centered at 20 pixels below the original                circle.            -   iii. 3 for the “top” region, which is a 12 pixel wide                circle drawn centered at 20 pixels above the original                circle.            -   iv. 0 for all other points in the image.        -   b. Five sets of pixels are calculated on the temporary            image. The average pixel value is calculated from the search            image (Red or Redness enhanced Red) for each set of pixels:            -   i. Top pixels, used to calculate AvgTop;            -   ii. Bottom Pixels, used to calculate AvgBot;            -   iii. Inner pixels, used to calculate AvgIn;            -   iv. Outer pixels (top and bottom), used to calculate                AvgOut;            -   v. Inner-bottom pixels (inner and bottom), used to                calculate AvgInBot.        -   c. Two ratios are calculated from these sets of pixels:            -   v. InOutRatio=AvgIn/AvgOut;            -   vi. BotTopRatio=min([AvgBot/AvgTop, AvgIn/AvgTop,                AvgInBot/AvgTop]).        -   d. The InOutRatio gives an estimate of how closely the            circle conforms to a low-intensity cervix-speculum boundary,            and the BotTopRatio helps to evaluate how well the circle            matches an intensity difference.        -   e. To be a valid speculum representation, a circle should            satisfy the following criterion:            -   (InOutRatio<0.70) OR (InOutRatio<0.92 AND                BotTopRatio<0.83).            -   If no circles meet this criterion, then the algorithm                detects NO inner speculum.        -   f. The inner circle representing the speculum is the circle            from step e that has the minimum value of InOutRatio.        -   g. If there is a resulting circle that has passed the            validation procedure, evaluate to verify it is not a false            positive by comparing the mean luminance on two portions of            the ROI, above the speculum and below the speculum.            -   vii. Glare, blood and os are removed from ROI to obtain                dROI, where dROI=ROI AND not(glareMsk) AND not(bloodMsk)                AND not(osMsk).            -   viii. Compute mean luminance, meanLTop, on dROI region                above circle.            -   ix. Compute mean luminance, meanLBot, on dROI region                below circle.            -   x. If meanLBot>0.8*meanLTop and the bottom-most point on                the inner circle is less than ¾ of the image height,                then the candidate is a false positive and is discarded.

Finally, the method 2112 in FIG. 108 includes final determination of thespecular segmentation mask in step 2128. The final segmentation mask iscomputed from the results of the inner and outer speculum searches. Ifthe outer search produces a satisfactory result and no inner search isdone, the final mask is the one computed by the outer speculum search.If the outer search produces a satisfactory result and an inner searchis performed which also produces a result, the final segmentation maskis the logical OR of the inner and outer masks. If the outer searchproduces no result but the inner search produces a result, the finalmask is the mask from the inner search. If neither search produces aresult, the final segmentation is empty, indicating that the algorithmhas determined that no speculum is present.

[VW]_(vid)

Step 1454 in FIG. 74 depicts the determination of a vaginal wall imagemask, [VW]_(vid), for an image of a tissue sample. [VW]_(vid) is used inhard-masking in the tissue characterization method 1438 of FIG. 74. FIG.109A depicts an exemplary image 2190 of cervical tissue used todetermine the corresponding vaginal wall image mask, [VW]_(vid), 2194shown in FIG. 109B.

In one embodiment, the vaginal wall mask detects vaginal walls andcervical edges, including formices and speculum blades. Here, the maskis determined using a filter shaped like a notch to emphasize thevaginal wall. This is similar to template matching in which the templateis present along one dimension and the filter is constant along theother dimension. This achieves a projection-like averaging.

After application of the filter in horizontal and vertical orientations,the resultant gradient images are thresholded and skeletonized. Aheuristic graph searching method connects disconnected edges, and theedges are extended to the bounds of the image to form a full mask. Oncethe edges are extended, the edge lines are shadowed outward from thecenter of the image to form the final vaginal wall segmentation mask,[VW]_(vid).

FIG. 110 is a block diagram 2218 depicting steps in a method ofdetermining a vaginal wall image mask, [VW]_(vid), for an image ofcervical tissue. The following describes the steps of the method 2218shown in FIG. 110, according to one embodiment.

The method 2218 in FIG. 110 includes preprocessing in step 2220. First,calculate or retrieve the glare, glareMsk, ROI, ROIMsk, and os, osMsk,segmentation masks. Calculate the luminance L from the RGB signal usingthe formula: L=0.299*R+0.587*G+0.114*B. Dilate glareMsk 4 times toobtain dilGlareMsk. Then, filter the RGB image using dilGlareMsk toperform run-length boundary interpolation as follows:

-   -   1. Raster scan each row of dilGlareMsk to find all beginnings        and ends of pixel runs.    -   2. For each pixel P(x,y) in a given run specified by beginning        point P(xb, y) and end point P(xe,y) in the intensity image,        replace P(x,y) by half the linearly interpolated value at P(x,y)        from P(xb,y) and P(xe,y).    -   3. Raster scan each column of dilGlareMsk to find all beginnings        and ends of pixel runs.    -   4. For each pixel P(x,y) in a given run specified by beginning        point P(x, yb) and end point P(x,ye) in the intensity image, add        to P(x,y) half the linearly interpolated value at P(x,y) from        P(x,yb) and P(x,ye).    -   5. Perform a 11×11 box car filter smoothing on dilGlareMsk        regions only.        Finally, smooth the filled RGB channels by filtering once with a        3×3 box car filter.

Next, the method 2218 in FIG. 110 includes gradient image processing insteps 2222, and 2224. First, create a notch filter for detecting thevaginal wall. The filter of length 22 is defined by the followingcoefficients: [1 1 1 1 2/3 1/3 0 −1/3 −2/3 −1 −1 −1 −1 −2/3 −1/3 0 1/32/3 1 1 1]. Then, normalize the filter: The average of the filtercoefficients is subtracted from the filter in order to make it azero-gain convolution kernel. Replicate rows 24 times to create a 22 by24 filter. Filter the luminance image L with the vaginal wall notchfilter to produce the vertical gradient image vGradImg. Filter theluminance image with the transpose of the notch filter to produce thehorizontal gradient image hGradImg. Clip gradient images to 0. Finally,perform the following thresholding and clean-up operations on each ofthe gradient images hGradImg and vGradImg:

-   -   1. Threshold the images at 975 to yield a binary object image.    -   2. Perform a binary component labeling using 4-way connectivity.    -   3. Compute regions statistics: area, centroid, major and minor        axis length.    -   4. Discard any object whose size is less than 1000 pixels.    -   5. Discard any object which is within 80 pixels of distance from        the center of the image.    -   6. Dynamically calculate the minimum allowable length,        MinAllowedLength, for each object based upon the distance of its        centroid (xCentroid, yCentroid) from the center of the image        (Cx, Cy) defined by Cx=(image width)/2 and Cy=(image height)/2.        Let x be the distance of the centroid to the center of the        image, x=sqrt((xCentroid−Cx)²+(yCentroid−Cy)²). MinAllowedLength        scales the minimum allowed distance from 250 (at the image        center) to 100 at the left or rightmost edge of the image and is        defined by: MinAllowedLength=250−(15*x/25).    -   7. Discard any object with a major axis length less than        MinAllowed Length.    -   8. Discard any object that is more than 50% outside of the        image's ROI.    -   9. Discard any object that covers more than 5% of the os.

Next, the method 2218 in FIG. 110 includes skeletonization in step 2226.The binary images resulting from step 2224 are processed with askeletonization algorithm that approximates the medial axis transform.The skeletonization algorithm works for either horizontal or verticaledges. For vertical edges, each row is scanned from left to right. Eachtime the pixel values transition from OFF to ON, the index of the ONpixel is remembered. If the first pixel in the row is ON, this qualifiesas a transition. When there is a transition from ON back to OFF, theindex of the last ON pixel is averaged with the index from the previousstep to give the center pixel in the ON region. If an ON region extendsto the last pixel in the row, then this last pixel is treated as atransition point. All pixels between and including the first and last ONpixels are turned off except the center pixel. For horizontal edges,each column is scanned from top to bottom. The same steps describedhereinabove are repeated for the columns instead of the rows.

Next, the method 2218 in FIG. 110 includes edge linking and extension insteps 2226, and 2228. The skeletonizations are processed with aheuristic graph-searching method which connects slight gaps in theskeletonized images and extends the edges to the image boundary. Thefollowing images and parameters are used by the edge linking algorithm:

-   -   Horizontal and vertical skeletonized edge image, vSkelImg,        hSkelImg    -   Input label matrix, LblMat. This is found by labeling matrix        output from the connected components analysis, where discarded        regions have been removed from the label matrix by setting their        pixel values back to 0.    -   Horizontal and vertical edge orientation, vEdgeOrient,        hEdgeOrient.    -   Skeletonized input label matrix, skLblMat. This is a copy of        LblMat where all the pixels which are OFF in the skeletonized        image are set to 0 in skLblMat.    -   Gap=16.0, the maximum allowable gap to fill in for a        disconnected edge.        The following are searching methods that are implemented.    -   1. Search for Edge Pixels: For both the horizontal and vertical        edge images, the images are raster searched to locate edges        within them.        -   a. The vertical edge image, vSkelImg, is searched by row            raster scanning to ensure that the first point in an edge is            encountered.        -   b. The horizontal edge image, hSkelImg, is searched by            column raster scanning to ensure that the first point in an            edge is encountered.        -   c. When a point is encountered, the algorithm references            skLblMat to see if that point has a positive label,            indicating that this edge has not yet been processed. If so,            the edge connection and edge extension routines described in            the steps below are executed starting from this point.    -   2. Edge Connection. The edge connection routine starts from the        point from which it is called. The routine keeps a list of the        points encountered in the edge. The search is executed only for        points with the same label in dilGlareMsk.        -   a. Create Label matrix skLblMat as described above.        -   b. Find second point:            -   i. Starting from the first point, do a search in a                rectangular region of size 2*(Gap+1.5)+1 centered about                the first point.            -   ii. The second point will be the point which is ON in                the edge image which is closest to the first point, and                which is not already part of any other linked edge (must                have same label value as the first point).            -   iii. Fill in the gap between the first point and the                second point. The Gap filling algorithm is described                below in step 3.            -   iv. If this edge begins at a point “sufficiently close”                (with respect to Gap) to another edge, set a flag to                prevent extension of the beginning of this edge.            -   v. If no second point is found, or if the second point                is part of another edge which has already been linked,                erase this edge in the output edge image (see Edge                Erasing description below) and in skLblMat, stop                processing this edge, and continue the loop to look for                the next edge.        -   c. Find the third point:            -   i. Starting from the second point, do a search in a                rectangular region of size 2*(Gap+1.5)+1 centered about                the second point.            -   ii. The third point will be the point which is ON in the                edge image which is closest to the second point, and                which is not already part of this or any other linked                edge (must have same label value as the first point).            -   iii. Fill in the gap between the second point and the                third point.            -   iv. If no third point is found, or if the third point is                part of another edge which has already been linked,                erase this edge in the output edge image, stop                processing this edge, and continue the loop to look for                the next edge.        -   d. After three points in this edge are discovered, there is            enough information to infer a search direction, and from            here on out all searches in the Edge Connection are            directional. Steps for computing the search location are            listed below.        -   e. Starting with the search for the fourth point, the            following steps are iteratively performed until no further            pixels in this edge can be found:            -   i. The search direction: North (N), South (S), East (E),                West (W), NorthEast (SE), NorthWest (NW), SouthEast (SE)                or SouthWest (SW) is computed by the steps described                below.            -   ii. Check the edge length, if it is greater than 2048,                break out of the loop because this edge must have looped                back upon itself.            -   iii. Find the next point in the given search direction:                If no further points were found, check to see if the                edge length is less than 120.                -   1. If edge length <120, erase edge and break out of                    this loop to continue the processing to find other                    edges (back to step l).                -   2. If edge length >=120, keep edge end break out of                    loop and continue with step f).            -   iv. Fill in the gap between the current point and the                new point.            -   v. If the new point belongs to an edge which was already                linked by this algorithm, do the following:                -   1. If the current edge is less than 40 pixels in                    length, erase this edge. Break out of the loop and                    continue searching for further edges (back to step                    1).                -   2. Otherwise, the edge will be kept, but a flag is                    set so that the end of this edge is not extended.                    Break out of the loop and continue with step f.            -   vi. Increment the edge length so that the new point                becomes the current point for the next iteration.            -   vii. Continue with step i) to continue processing.        -   f. At this point, a valid edge has been detected. This edge            will then be extended in the both directions to the boundary            of the image unless either edge (or both) is flagged for not            extending. The edge extension steps are described below in            step 5.        -   g. Check to see if an extension passed through the center of            the image (defined by a circle of radius 80 centered at the            geometrical center of the image).            -   i. If an extension did pass through the center of the                image, erase this edge and all of its extensions.            -   ii. Otherwise, relabel this edge in the Label matrix to                have value −1, and draw the extensions on the output                edge image, simultaneously labeling the corresponding                pixels in the Label matrix with value −2.    -   3. Gap Filling method:        -   a. Check to see if there is no gap, i.e. if the edge is            already connected. Where (x1,y1) and (x2,y2) are the new            point and the current point, if abs(x1−x2)<2 and            abs(y1−y2)<2, then there is no gap to fill, and the Gap            Filling processing stops.        -   b. Remove the “New pixel” from the edge vectors so that it            can be replaced with a set of filled-in pixels.        -   c. Check for special cases where x1=x2 or y1=y2. In either            of those two cases, the Gap Filling is accomplished by            simply turning on every pixel which lies between the two            pixels in the output Edge image.        -   d. For the case where x1 is not equal to x2 and y1 not equal            to y2, a diagonal line needs to be drawn to fill the gap.            -   i. This is done first by computing an equation for the                line which connects the two points.            -   ii. If the slope is greater than 1, iterate from y=y1 to                y2, and compute the x value for each y value. For each                (x,y) turn on the corresponding pixel in the output Edge                image and in skLabMat.            -   iii. If the slope is less than 1, iterate from x=x1 to                x2, and compute the y value for each x value. For each                (x,y) turn on the corresponding pixel in the output Edge                image and in skLabMat.        -   e. Finally, all of the new pixels are added to the edge            vectors in order from the current pixel to the new one. The            corresponding pixels in skLabMat are set to the label value            −2.    -   4. Computing Search Direction:        -   a. Two pixel locations are used to infer a search direction.            -   i. The first point is the geometric average of the two                most current pixels in the edge.            -   ii. If there arc less than 6 pixels in the edge, the                second point is the average of the first and second                pixels in the edge.            -   iii. If there are more than 6 pixels in the edge, the                second point is the average of the fifth and sixth most                current pixels in the edge.        -   b. For the two pixels (x1,y1) and (x2,y2), the search            direction is computed as follows:            -   i. Compute the angle formed by the two points using the                ATAN2 function:            -   angle=atan2(y1−y0,x1−x0)*180/π;        -   ii. If angle is in the interval [−22.5, 22.5], the search            direction is E.        -   iii. If angle is in the interval [22.5, 67.5], the search            direction is SE.        -   iv. If angle is in the interval [67.5, 112.5], the search            direction is S.        -   v. If angle is in the interval [112.5, 157.5], the search            direction is SW.        -   vi. If angle is in the interval [−67.5, −22.5], the search            direction is NE.        -   vii. If angle is in the interval [−112.5, −67.5], the search            direction is N        -   viii. If angle is in the interval [−157.5, −112.5], the            search direction is E.        -   ix. Otherwise, the search direction is W.    -   5. Edge Extension:        -   a. It is the default to extend both the beginning and end of            the edge. However, during the edge connection steps, if it            is discovered that the edge originates close to a different            edge, the edge is connected to the different edge and is not            extended. If an edge ends by merging with another edge, the            end of the edge is not extended.        -   b. For both the beginning and the end of the edge:            -   i. For Vertically oriented edge images (vEdgeOrient):                -   1. If the y-coordinate for the first/last point of                    the edge is less than the image height/6 or greater                    than 5*height/6, extend the beginning/end of the                    edge using the local slope method (described below).                -   2. Otherwise, extend the beginning/end of the edge                    using the global slope method (described below).            -   ii. For Horizontally oriented edge images (HEdgeOrient):                -   1. If the x-coordinate for the first/last point of                    the edge is less than the image width/6 or greater                    than 5* width/6, extend the beginning/end of the                    edge using the local slope method (described below).                -   2. Otherwise, extend the beginning/end of the edge                    using the global slope method (described below).        -   c. Local Slope Extension: This method uses the slope of the            edge near its beginning/end to determine the slope of the            extending line.            -   i. Compute two points for slope computation:                -   1. the average of the four pixels from the                    beginning/end of the edge; and                -   2. the average of the 6th through 9th pixels from                    the beginning/end of the edge.            -   ii. Using the two computed points, the edge is extended                from its beginning/end point using a line of the                computed slope until it reaches the edge of the image.        -   d. Global Slope Extension: this method uses pixel values            between 20% and 80% of the length along the edge to guess            the “average” slope of this edge. Then the beginning/end of            the edge is extended using this slope.            -   i. If the edge has edgeLen pixels in it, select the                points in the edge with the following indices:                -   1. begIDX=round(edgeLen*0.2); pointa=edge(begIDX);                -   2. endIDX=round(edgeLen*0.8); pointB=edge(endIDX).            -   ii. Compute the slope using pointA and pointB, and use a                line of this slope to extend from the beginning/end of                this edge.        -   e. After the extension is computed, the extended pixels are            turned ON in the output edge image, and the corresponding            pixels in skLabMat are assigned value −2.    -   6. Edge Erasing.        -   When an edge is to be erased check to verify that for each            pixel in the edge and its extension the label for the pixel            is >0. If so, set the value in the output Edge image and the            label matrix to 0. This method assures that pixels in            another edge that has already been linked are not erased            (the two edges might have crossed).

Finally, the method 2218 in FIG. 110 includes mask computation in step2230. The output of the Edge Linking algorithm is used to generate thevaginal wall mask in the following way:

-   -   1. Vertical connected-edge image: VConnImg, a cumulative sum, is        calculated for each row, starting from the center and extending        both to the left and to the right.    -   2. Horizontal connected-edge image: HConnImg, a cumulative sum,        is calculated for each column, starting from the center and        extending both upward and downward.    -   3. The two cumulative sums are thresholded at >=1 and OR-ed        together to yield the final vaginal wall mask.

[FL]_(vid)

Step 1454 in FIG. 74 depicts the determination of a fluid-and-foam mask,[FL]_(vid), for an image of a tissue sample. This mask identifies fluidand foam regions appearing on tissue samples and is used in hard maskingin the tissue characterization method 1438 of FIG. 74. FIG. 111A depictsan exemplary image 2234 of cervical tissue used to determine thecorresponding fluid-and-foam image mask, [FL]_(vid), 2238 shown in FIG.111B.

In one embodiment, the fluid-and-foam image mask identifies regionswhere excess fluids and/or foam collect on cervical tissue. Excess fluidor foam can collect near the speculum, around or in the os, and/or inthe folds between the vaginal walls and the cervix, for example. Oneembodiment of the fluid-and-foam image mask, [FL]_(vid), uses a measureof whiteness and a measure of blue-greenness to identify regions offluid/foam. After extracting white and blue-green color features,thresholding and validation is performed to produce the finalfluid-and-foam image mask, [FL]_(vid).

FIG. 112 is a block diagram 2258 depicting steps in a method ofdetermining a fluid-and-foam image mask, [FL]_(vid), for an image ofcervical tissue. The following describes the steps of the method 2258shown in FIG. 112, according to one embodiment.

The method 2258 in FIG. 112 includes preprocessing in step 2260. First,remove glare from the RGB image. Retrieve or compute glare mask,glareMsk. Dilate glareMsk 4 times to obtain dilGlareMsk. Next, retrieveor compute ROI mask, ROIMsk. Finally, smooth each of the RGB channelusing a 3×3 box car filter to remove noise.

Next, the method 2258 in FIG. 112 includes image color featurecalculation in step 2262. This step computes a “whiteness” image, Wimg,and a “green-blueness” image, GBImg. First, calculate the luminance Lfrom the RGB signal using the formula: L=0.299*R+0.587*G+0.114*B. Next,compute, normalize and threshold Wimg as follows:

-   -   1. WImg=abs((R−G)/(R+G))+abs((R−B)/(R+B))+abs((G−B)/(G+B)).        -   This operation is a pixel-wise operation and is performed on            each pixel sequentially.    -   2. Normalize Wimg: WImg=3−Wimg.    -   3. Set low luminance pixels to 0 (low luminance pixels are        unlikely to be in the fluid and foam regions):        -   If L<mean(L), WImg=0.            Finally, compute, normalize and threshold BGImg as follows:    -   1.        BGImg=(abs((R+30−G)/(R+30+G))+abs((R+30−B)/(R+30+B))+abs((G−B)/(G+B))).        -   This operation is a pixel-wise operation and is performed on            each pixel sequentially.    -   2. Normalize BGImg, BGImg=3−BGImg.    -   3. Set low luminance pixels to 0 (low luminance pixels are        unlikely to be in the fluid and foam regions):        -   If L<0.65*mean(L), BGImg=0.

Next, the method 2258 in FIG. 112 includes processing and segmentingbright green-bluish regions in steps 2264, 2266, 2268, 2270, 2272, 2274,and 2276. These steps are performed as follows:

-   -   1. Retrieve or compute glare mask, glareMsk.    -   2. Fill glare regions of BGImg using glareMsk to perform        run-length boundary interpolation as follows:        -   a. Raster scan each row of glareMsk to find all beginnings            and ends of pixel runs.        -   b. For each pixel P(x,y) in a given run specified by            beginning point P(xb, y) and end point P(xe,y) in the            intensity image, replace P(x,y) by half the linearly            interpolated value at P(x,y) from P(xb,y) and P(xe,y).        -   c. Raster scan each column of glareMsk to find all            beginnings and ends of pixel runs.        -   d. For each pixel P(x,y) in a given run specified by            beginning point P(x, yb) and end point P(x,ye) in the            intensity image, add to P(x,y) half the linearly            interpolated value at P(x,y) from P(x,yb) and P(x,ye).    -   3. Eliminate low intensity areas using a threshold of 1.5:        -   If BGImg<1.5, BGImg=1.5.    -   4. Rescale the BGImg to [0, 1]:        -   BGImg=BGImg−min(BGImg))/(3−min(BGImg).    -   5. Compute thresholds from image statistics and perform        thresholding.        -   a. Compute image mean intensity, Imean, for BGImg>0.        -   b. Compute image standard deviation of intensity, IstdDev,            for BGImg>0. Compute threshold thGB,            thGB=Imean+1.63*IstdDev.        -   c. Apply threshold limits:            -   if thGB<0.80, thGB=0.80;            -   if thGB>0.92, thGB=0.92.        -   d. Threshold to get the initial green-bluish fluid and foam            mask GBMask            -   if BGImg>thGB, then                -   GBMask=1;            -   else                -   GBMask=0.    -   6. Perform morphological processing to fill small holes and        smooth boundaries of the found regions in GBMask:        -   a. Dilate the segmentation mask GBMask twice,            GBMask=dil(GBMask, 2).        -   b. Erode the resultant mask three times,            GBMask=erode(GBMask, 3).        -   c. Dilate the resultant mask once, GBMask=dil(GBMask, 1).    -   7. Perform binary region labeling and small region removal:        -   a. Perform a connected components labeling, described above,            to label all found regions.        -   b. Compute each region area, area, and eccentricity, ecc.        -   c. Remove small and round regions and small line segments            that are not likely to be the fluid and foam regions: If            ((area<1000) AND (ecc<0.70)) OR ((area<300) AND (ecc>0.70))            OR (area<1000), remove region.    -   8. Green-Bluish feature validation for each found region is        based on the original RGB values:        -   a. For each found region, retrieve the mask, Imsk, and            compute the mean intensities within the region for each of            the red, green and blue channels as MRed, MGreen and Mblue.        -   b. If the found region is tissue-like, remove the region: if            [(MGreen−MRed)+(MBlue−MRed)]<−5 remove region.        -   c. If the found region is too blue, remove the region: if            (MBlue>MGreen+15) remove region.    -   9. The final green-bluish fluid and foam mask, FGBMask, is        calculated by performing a flood-fill of “on” valued regions of        GBMask from step 5 with seeds in the validated regions from step        6 and step 7.

Next, the method 2258 in FIG. 112 includes processing and segmentingpure white regions in steps 2278, 2280, 2282, 2284, 2286, 2288, and2290. These steps are performed as follows:

-   -   1. Retrieve glare mask, glareMsk and ROI mask, ROIMsk.    -   2. Fill glare regions of WImg using glareMsk to perform        run-length boundary interpolation as follows:        -   a. Raster scan each row of glareMsk to find all beginnings            and ends of pixel runs.        -   b. For each pixel P(x,y) in a given run specified by            beginning point P(xb, y) and end point P(xe,y) in the            intensity image, replace P(x,y) by half the linearly            interpolated value at P(x,y) from P(xb,y) and P(xe,y).        -   c. Raster scan each column of glareMsk to find all            beginnings and ends of pixel runs.        -   d. For each pixel P(x,y) in a given run specified by            beginning point P(x, yb) and end point P(x,ye) in the            intensity image, add to P(x,y) half the linearly            interpolated value at P(x,y) from P(x,yb) and P(x,ye).    -   3. Compute WImg mean, mWImg, and standard deviation, stdWImg.    -   4. Eliminate low intensity areas: if WImg<mWImg−0.1*stdWImg,        WImg=mWImg−0.1*stdWImg.    -   5. Rescale the WImg to [0, 1]:        -   WImg=WImg−min(WImg))/(3−min(WImg).    -   6. Compute thresholds from image statistics and perform        thresholding:        -   a. Compute image mean intensity, Imean, for WImg>0.        -   b. Compute image standard deviation of intensity, IstdDev,            for WImg>0.        -   c. Compute threshold thW,            -   thW=Imean+1.10*IstdDev.        -   d. Threshold to get the initial green-bluish fluid and foam            mask WMask:            -   if ((WImg>thW) AND (pixel is included in ROIMsk)), then                -   WMask=1;            -   else                -   WMask=0.    -   7. Perform morphological processing to fill small holes and        smooth boundaries of the found regions in WMask:        -   a. Erode the segmentation mask WMask twice,            WMask=erode(WMask, 2).        -   b. Dilate the resultant mask three times,            WMask=dilate(WMask, 3).    -   8. Perform binary region labeling and small region removal:        -   a. Perform a connected components labeling, as described, to            label all found regions.        -   b. Compute each region area, area.        -   c. Remove small regions that are not likely to the fluid and            foam regions: If (area<300) remove the region from the            region list.    -   9. Whiteness feature validation for each found region based on        the original RGB values:        -   a. For each found region, retrieve the mask, iMsk, and            compute the mean intensities within the region for each of            the red, green and blue channels as iMRed, iMGreen and            iMBlue.        -   a. Dilate iMsk five times to obtain iD1Msk=dilate(iMsk, 5).        -   b. Compute the perimeter pixels iPeriMsk from iD1Msk:            iPeriMsk=not (erod(iD1Msk, 1)) AND (iD1Msk)), 1).        -   c. Dilate iPeriMsk three times to get the outer mask:            iD2Msk=dilate (iPeriMsk, 3).        -   d. Compute mean intensities on iD2Msk for each of the R, G            and B channels as perimeter (Outer) means: pMRed, pMGreen            and pMBlue.        -   e. Compute the Inner region green-blueness:            innerGB=(iMGreen−iMRed)+(iMBlue−iMRed).        -   f. Compute the Inner region whiteness:            innerW=3.0−(abs((iMRed−iMGreen)/(iMRed+iMGreen))+abs((iMGreen−iMBlue)/(iMGreen+iMBlue))+abs((iMBlue−iMRed)/(iMBlue+iMRed))).        -   g. Compute the Outer region whiteness:            outerW=3.0−(abs((pMRed−pMGreen)/(pMRed+pMGreen))+abs((pMGreen−pMBlue)/(pMGreen+pMBlue))+abs((pMBlue−pMRed)/(pMBlue+pMRed))).        -   h. Compute the Outer region redness:            outerRed=(pMRed−pMGreen)+(pMRed−pMBlue).        -   i. Apply general whiteness validation rule: if            (((innerGB<10) AND (outerRed>25)) OR (outerW>(innerW−0.1)),            then:            -   -   set is Fluid to 0, since it is not likely to be a                    fluid and foam region; else, Set is Fluid to 1.        -   j. Very white fluid-foam validation rule: If            ((innerW>(outerW+0.16)) set is Fluid to 1.        -   k. Very high inner green bluish fluid-foam validation rule:            If (innerGB>10) set is Fluid to 1.    -   10. The final white fluid-foam mask fWMask is calculated by        performing a flood-fill of “on” valued regions of Mask from step        8 with seeds in the validated regions (is Fluid=1) from step 9.

Finally, the method 2258 in FIG. 112 includes constructing the finalfluid-foam mask. The final fluid-foam mask is a logical “OR” of the twosegmented and validated masks as follows: FluidFoamMask=fBGMask ORfWMask.

Classifiers

In one embodiment, the tissue characterization system 100 of FIG. 1comprises using broadband reflectance data obtained during a spectralscan of regions (interrogation points) of a tissue sample to determineprobabilities that a given region belongs in one or moretissue-class/state-of-health categories. In one embodiment,probabilities of classification are determined as a combination ofprobabilities computed by two different statistical classifiers. The twoclassifiers are a DASCO classifier (discriminant analysis with shrunkencovariances), and a DAFE classifier (discriminant analysis featureextraction). The DASCO classifier (step 1484, FIG. 74) uses a principalcomponent analysis technique, and the DAFE classifier (step 1482, FIG.74) uses a feature coordinate extraction technique to determineprobabilities of classification.

The embodiment shown in FIG. 74 applies a necrosis mask 1424 and a hard“indeterminate” mask 1426 to a set of arbitrated broadband spectral datato eliminate the need to further process certain necrotic andindeterminate interrogation points in the classification steps 1482,1484, 1486. After determining statistical classification probabilitiesin step 1486, the embodiment of FIG. 74 applies a soft “indeterminate”mask 1428 as well as the NED (no evidence of disease) classificationresult 1430 in order to obtain a final characterization 1432 of eachinterrogation point on the tissue sample as Necrotic, CIN 2/3, NED, orIndeterminate.

The statistical classifiers in steps 1482 and 1484 of FIG. 74 eachdetermine respective probabilities that a given region belongs to one ofthe following five tissue-class/state-of-health categories: (1) Normalsquamous (N_(s)), (2) CIN 1 (C₁), (3) CIN 2/3 (C₂₃), (4) Metaplasia (M),and (5) Normal columnar (C_(ol)) tissue. Other embodiments use one ormore of the following tissue classes instead of or in addition to thecategories above: CIN 2, CIN 3, NED (no evidence of disease), andcancer. The category with the highest computed probability is thecategory that best characterizes a given region according to theclassifier used. In one alternative embodiment, other categories and/oranother number of categories are used. The results of the twostatistical classifiers are combined with the NED mask classification,along with the hard and soft “indeterminate” masks, to obtain a finalcharacterization for each interrogation point 1432.

In one embodiment, statistical classification includes comparing testspectral data to sets of reference spectral data (training data)representative of each of a number of classes. A collection of referencespectra from the same tissue class is a class data matrix. For example,a class data matrix T_(j) comprising reference spectra (training data)from samples having known class j is expressed as in Equation 96 asfollows:

$\begin{matrix}{T_{j} = {\begin{bmatrix}{S_{1}\left( \lambda_{1} \right)} & {S_{1}\left( \lambda_{2} \right)} & \cdots & {S_{1}\left( \lambda_{p} \right)} \\{S_{2}\left( \lambda_{1} \right)} & {S_{2}\left( \lambda_{2} \right)} & \cdots & {S_{2}\left( \lambda_{p} \right)} \\\vdots & \vdots & \cdots & \vdots \\{S_{n_{j}}\left( \lambda_{1} \right)} & {S_{n_{j}}\left( \lambda_{2} \right)} & \cdots & {S_{n_{j}}\left( \lambda_{p} \right)}\end{bmatrix} \equiv \begin{bmatrix}{S_{1}(\lambda)} \\{S_{2}(\lambda)} \\\vdots \\{S_{n_{j}}(\lambda)}\end{bmatrix}}} & (96)\end{matrix}$where class j contains n_(j) reference spectra, S(λ), and each referencespectra, S(λ)=[S(λ₁),S(λ₂), . . . . , S(λ_(p))], is a p-dimensionalvector where p is the number of wavelengths in a measured spectrum. Theclass data matrix T_(j) has associated with it a class mean vector μ_(j)(a 1-by-p vector) and a class covariance matrix C_(j) (a p-by-p matrix)as shown in Equations 97–99 as follows:

$\begin{matrix}{{{\mu_{j}(\lambda)} \equiv \mu_{j}} = \left\lbrack \begin{matrix}{\frac{1}{n_{j}}{\sum\limits_{k = 1}^{n_{j}}{S_{k}\left( \lambda_{1} \right)}}} & {\frac{1}{n_{j}}{\sum\limits_{k = 1}^{n_{j}}{S_{k}\left( \lambda_{2} \right)}}} & \cdots & {\frac{1}{n_{j}}{\sum\limits_{k = 1}^{n_{j}}{S_{k}\left( \lambda_{p} \right)}}}\end{matrix} \right\rbrack} & (97) \\{C_{j} = {\frac{1}{n_{j} - 1}{\sum\limits_{k = 1}^{n_{j}}{\left( {{S_{k}(\lambda)} - \mu_{j}} \right)^{T}\left( {{S_{k}(\lambda)} - \mu_{j}} \right)}}}} & (98) \\{\mspace{31mu}{\equiv {\frac{1}{n_{j} - 1}\left( {T_{j} - M_{j}} \right)^{T}\left( {T_{j} - M_{j}} \right)}}} & \; \\{M_{j} = \begin{bmatrix}\mu_{j} \\\mu_{j} \\\vdots \\\mu_{j}\end{bmatrix}_{n_{j} \times p}} & (99)\end{matrix}$Statistical tissue classification uses reference data to determine for agiven test spectrum to which class(es) and with what probabilit(ies)that test spectrum can be assigned.

The broadband data used in the statistical classifiers in steps 1482 and1484 are wavelength truncated. For the DASCO classifier (step 1484),only training data and testing data that corresponds to wavelengthsbetween about 400 nm and about 600 nm are used. For the DAFE classifier(step 1482), only training data and testing data that correspond towavelengths between about 370 nm and about 650 nm are used. Onealternative embodiment uses different wavelength ranges. The trainingdata include reference broadband reflectance data from interrogationpoints having a known classification in one of the five states ofhealth, and the testing data include broadband reflectance data from aregion having an unknown classification.

The discriminant analysis feature extraction (DAFE) method of step 1482in FIG. 74 transforms a measurement of high dimension into a featurespace of lower dimension. Here, the feature space is the orthogonalprojection in the direction of maximal data discrimination. The DAFEmethod includes constructing feature coordinates by computing thefeature space projection matrix. The projection matrix requires theinversion of the pooled within-groups covariance matrix, C_(pool). WhereT₁, T₂, . . . , T_(g) are training matrices for classes 1 through g(here, for example, g=5), the number of reference spectra in a givenclass, n_(j), may be less than the number of wavelengths in a measuredspectrum, p; and C_(pool) is therefore singular and cannot be inverted.

Thus, in one embodiment of the DAFE method of step 1482, the spectralmeasurements are subsampled so that a covariance matrix can be computed.In one embodiment, a subsampling rate, n_(z), is determined according toEquation 100:

$\begin{matrix}{n_{z} = {{\max\left( \left\lfloor {\frac{p}{n_{1}},\frac{p}{n_{2}},\cdots\mspace{11mu},\frac{p}{n_{g}}} \right\rfloor \right)} + 1}} & (100)\end{matrix}$where p is the number of wavelengths in a measured spectrum; n₁, n₂, . .. , n_(g) represent the numbers of reference spectra in each of classes1, 2, . . . , g, respectively; and └ ┘ indicates the “nearest integer”function. Typically, n_(z)=2 or 3, but values up to about 10 do notgenerally remove too much information from a measured reflectancespectrum, and may also be considered. After subsampling, thenon-singular pooled covariance matrix, C_(pool), is computed accordingto Equation 101 as follows:

$\begin{matrix}\begin{matrix}{C_{pool} = {\frac{1}{n - g}{\sum\limits_{k = 1}^{g}{\left( {n_{k} - 1} \right) \cdot C_{k}}}}} \\{n = {\sum\limits_{k = 1}^{g}n_{k}}}\end{matrix} & (101)\end{matrix}$where n_(k) is the number of reference spectra in class k; and C_(k) isthe covariance matrix for class k. Then, the between-groups covariance,C_(btwn), is computed according to Equation 102:

$\begin{matrix}\begin{matrix}{C_{btwn} = {\frac{1}{g}{\sum\limits_{k = 1}^{g}{{n_{k} \cdot \left( {\mu_{k} - \overset{\_}{\mu}} \right)^{T}}\left( {\mu_{k} - \overset{\_}{\mu}} \right)}}}} \\{\overset{\_}{\mu} = {\frac{1}{n}{\sum\limits_{k = 1}^{g}{n_{k} \cdot \mu_{k}}}}} \\{n = {\sum\limits_{k = 1}^{g}n_{k}}}\end{matrix} & (102)\end{matrix}$

Next, the maxtrix P=C_(pool) ⁻¹·C_(bwtn) is formed and singular valuedecomposition is applied to obtain the following:P=UDV^(T)  (103)Let U_(g−1) equal the first g−1 columns of the orthogonal matrix ofsingular values U as follows:

$\begin{matrix}{U = {\left. \begin{bmatrix}u_{11} & u_{12} & \cdots & u_{1,{g - 1}} & \cdots & u_{1p} \\u_{21} & u_{22} & \cdots & u_{2,{g - 1}} & \cdots & u_{2p} \\\vdots & \vdots & \cdots & \vdots & \cdots & \vdots \\u_{p,1} & u_{p,2} & \cdots & u_{p,{g - 1}} & \cdots & u_{p,p}\end{bmatrix}\Rightarrow U_{g - 1} \right. = \begin{bmatrix}u_{11} & u_{12} & \cdots & u_{1,{g - 1}} \\u_{21} & u_{22} & \cdots & u_{1,{g - 1}} \\\vdots & \vdots & \cdots & \vdots \\u_{p,1} & u_{p,2} & \ldots & u_{p,{g - 1}}\end{bmatrix}}} & (104)\end{matrix}$Then, the feature projection, mapping measured space into feature space,is obtained via right-multiplication by U_(g−1).

The DAFE classification algorithm (step 1482 of FIG. 74) proceeds asfollows. Let {circumflex over (T)}₁, {circumflex over (T)}₂, . . . ,{circumflex over (T)}_(g) be the wavelength reduced, subsampled training(class data) matrices and Ŝ(λ) be the corresponding wavelength reduced,subsampled test spectrum. The matrices {circumflex over (T)}_(j) andŜ(λ) are projected into feature space as follows:{circumflex over (T)} _(j)

{circumflex over (T)} _(j) ·U _(g−1) ≡V _(j)Ŝ(λ)

Ŝ(λ)·U _(g−1) ≡x  (105)Next, the group mean vectors, group covariance matrices, and pooledwithin-groups covariance matrix are computed using the projectionmatrix, V_(j), in Equation 105, and using Equations 97, 98, and 101 asshown in Equations 106–108:μ_(j)=mean(V _(j))  (106)C _(j) =cov(V _(j))  (107)

$\begin{matrix}{C_{pool} = {\frac{1}{n - g}{\sum\limits_{j = 1}^{g}{\left( {n_{j} - 1} \right) \cdot C_{j}}}}} & (108)\end{matrix}$Then, the Friedman matrix is calculated using the Friedman parameters γand λ according to Equation 109 as follows:

$\begin{matrix}\begin{matrix}{{{Fr}_{j}\left( {\gamma,\lambda} \right)} = {{\left( {1 - \gamma} \right)\left\lbrack {{\left( {1 - \lambda} \right)C_{j}} + {\lambda\; C_{pool}}} \right\rbrack} +}} \\{\frac{\gamma}{g - 1}{{{tr}\left\lbrack {{\left( {1 - \lambda} \right)C_{j}} + {\lambda\; C_{pool}}} \right\rbrack} \cdot I_{{({g - 1})} \times {({g - 1})}}}}\end{matrix} & (109)\end{matrix}$In one embodiment, γ=0 and λ=0.5. Next, the Mahalanobis distance,d_(j)(x), is determined from the test spectrum to each data classaccording to Equation 110:d _(j) ²(x)=(x−μ _(j))·Fr _(j) ⁻¹(γ,λ)·(x−μ _(j))^(T)  (110)The Mahalanobis distance is a (1-by-1) number. Next, the Bayes' score iscomputed according to Equation 111:br _(j)(x)=d _(j) ²(x)−2 ln(r _(j))+ln (|det(Fr _(j)(γ,λ)|)  (111)The index j at which the minimum Bayes' score is attained indicates theclassification having the highest probability for the test point inquestion. The DAFE probability of classification for class j can becomputed for any of the g classifications according to Equation 112:

$\begin{matrix}\begin{matrix}{{{Prob}\left( {x \in {{Class}\mspace{14mu} j}} \right)} = \frac{\exp\left( {{- \frac{1}{2}}{{br}_{j}(x)}} \right)}{\sum\limits_{k = 1}^{g}{\exp\left( {{- \frac{1}{2}}{{br}_{k}(x)}} \right)}}} \\{= \frac{\frac{r_{j}}{{\det\left( {{Fr}_{j}\left( {\gamma,\lambda} \right)} \right)}} \cdot {\exp\left( {{- \frac{1}{2}}{d_{j}^{2}(x)}} \right)}}{\sum\limits_{k = 1}^{g}{\frac{r_{k}}{{\det\left( {{Fr}_{k}\left( {\gamma,\lambda} \right)} \right)}} \cdot {\exp\left( {{- \frac{1}{2}}{d_{k}^{2}(x)}} \right)}}}}\end{matrix} & (112)\end{matrix}$

DAFE classification probabilities are computed thusly for each of theinterrogation points having a test reflectance spectrum, S(λ), that isnot eliminated in the Necrosis masking step (1424) or the hard“indeterminate” masking step (1426) in the embodiment shown in FIG. 74.

Step 1484 in FIG. 74 is the DASCO (discriminant analysis with shrunkencovariances) method. Like the DAFE method of step 1482, the DASCO methodreduces the dimensionality of the measured space by transforming it intoa lower dimensional feature space. DASCO differs from DAFE in that thefeature space for the DASCO method is along orthogonal directions ofmaximal variance, not (necessarily) maximal discrimination. Also, DASCOuses two Mahalanobis distances, not just one. The first distance is thedistance to feature centers in primary space and the second distance isthe distance to feature centers in secondary space.

In one embodiment, the DASCO method (step 1484) proceeds as follows.First, a collection {T₁, T₂, . . . , T_(g)} of n_(j)-by-p trainingmatrices is obtained from reference (training) broadband arbitratedreflectance measurements. The amount of reflectance spectral dataobtained from a test region (interrogation point), as well as the amountof training data, are reduced by truncating the data sets to includeonly wavelengths between 400 nm and 600 nm.

Next, the training data and test data are scaled using mean scaling(mean centering) as follows:

$\begin{matrix}{\left. T_{j}\mapsto{\left( {T_{j} - M_{j}} \right) \equiv Y_{j}} \right.,{{{where}\mspace{14mu} M_{j}} = \begin{bmatrix}\mu_{j} \\\mu_{j} \\\vdots \\\mu_{j}\end{bmatrix}_{n_{j} \times p}}} & (113)\end{matrix}$S(λ)

S(λ)−μ_(j) ≡S _(j)  (114)

where j=1, 2, . . . , g and g is the total number oftissue-class/state-of-health classes. The number of principal componentsin primary space is n_(p), and the number of principal components insecondary space is n_(s). The total number of components is n_(t). Inone embodiment, n_(p)=3, n_(s)=1, and n_(t)=4.

Next, the first n_(t) principal component loadings and scores arecomputed. This involves computing the singular value decomposition ofthe mean scaled training data matrix Y_(j) from Equation 113, asfollows:Y_(j)=U_(j)D_(j)V_(j) ^(T)  (115)A similar computation was made in Equation 104. Let V_(j,n) _(t) be thematrix comprised of the first n_(t) columns of V_(j). The loadings andscores for Y_(j) are therefore indicated, respectively, in Equations 116and 117, as follows:Ld_(j)=V_(j,n) _(t)   (116)sc _(j) =Y _(j) ·V _(j,n) _(t) ≡Y _(j) ·Ld _(j)  (117)where Ld_(j) is a p-by-n_(t) matrix, and sc_(j) is an n_(j)-by-n_(t)matrix.

The next step in the DASCO method is to compute the class mean scoresand covariances. First, the class mean vector in primary space, v_(j,p),and the class mean vector in secondary space, v_(j,s), are computed asfollows:v _(j)=mean(sc _(j)) (the mean is computed analogously to μ_(j) inEquation 97)  (118)v_(j)≡└v_(j,1), v_(j,2), . . . , v_(j,n) _(p) ,v_(j,n) _(p+1) ,v_(j,n)_(p+2) , . . . , v_(j,n) _(p) _(+n) _(s)┘≡└v_(j,p),v_(j,s)┘=v_(j,p)⊕v_(j,s)  (119)where v_(j,p)=└v_(j,1),v_(j,2), . . . , v_(j,n) _(p) ┘ andv_(j,s)=└v_(j,n) _(p+1) ,v_(j,n) ₊₂ , . . . , v_(j,n) _(p) _(+n) _(s)┘  (120)Next, C_(j)=cov(sc_(j)) is defined as the class covariance matrixanalogous to that in Equation 100. In a manner similar to thecomputation of the primary and secondary space class mean vectors above,C_(j) is decomposed into the primary (C_(j,p)) and secondary (C_(j,s))space covariance matrices according to Equations 121–124 as follows:C_(j)=C_(j,p)⊕C_(j,s)  (121)

$\begin{matrix}{C_{j} = \begin{bmatrix}{c_{11}(j)} & {c_{12}(j)} & \cdots & {c_{1,n_{p}}(j)} & {c_{1,{n_{p} + 1}}(j)} & {c_{1,{n_{p} + 2}}(j)} & \cdots & {c_{1,{n_{p} + n_{s}}}(j)} \\{c_{21}(j)} & {c_{22}(j)} & \cdots & {c_{2,n_{p}}(j)} & {c_{2,{n_{p} + 1}}(j)} & {c_{2,{n_{p} + 2}}(j)} & \cdots & {c_{2,{n_{p} + n_{s}}}(j)} \\\vdots & \vdots & \cdots & \vdots & \vdots & \vdots & \cdots & \vdots \\{c_{n_{t},1}(j)} & {c_{n_{t},2}(j)} & \cdots & {c_{n_{t},n_{p}}(j)} & {c_{n_{t},{n_{p} + 1}}(j)} & {c_{n_{t},{n_{p} + 2}}(j)} & \cdots & {c_{n_{t},{n_{p} + n_{s}}}(j)}\end{bmatrix}} & (122) \\{C_{j,p} = \begin{bmatrix}{c_{11}(j)} & {c_{12}(j)} & \cdots & {c_{1,n_{p}}(j)} \\{c_{21}(j)} & {c_{22}(j)} & \cdots & {c_{2,n_{p}}(j)} \\\vdots & \vdots & \cdots & \vdots \\{c_{n_{t},1}(j)} & {c_{n_{t},2}(j)} & \cdots & {c_{n_{t},n_{p}}(j)}\end{bmatrix}} & (123) \\{C_{j,s} = \begin{bmatrix}{c_{1,{n_{p} + 1}}(j)} & {c_{1,{n_{p} + 2}}(j)} & \cdots & {c_{1,{n_{p} + n_{s}}}(j)} \\{c_{2,{n + 1}}(j)} & {c_{2,{n_{p} + 2}}(j)} & \cdots & {c_{2,{n_{p} + n_{s}}}(j)} \\\vdots & \vdots & \cdots & \vdots \\{c_{n_{t},{n_{p} + 1}}(j)} & {c_{n_{t},{n_{p} + 2}}(j)} & \cdots & {c_{n_{t},{n_{p} + n_{s}}}(j)}\end{bmatrix}} & (124)\end{matrix}$

Next, the scaled test spectrum from Equation 114 is projected into eachprincipal component space according to Equation 125:x(j)=Ld _(j) ·S _(j)  (125)Then, x(j) is decomposed into primary and secondary space vectors asfollows:x(j)≡[x ₁(j), x ₂(j), . . . , x _(n) _(t) (j)]=x _(j,p) ⊕x _(j,s)  (126)where x_(j,p)=[x₁(j), x₂(j), . . . , x_(n) _(p) (j)] is the projectionof x(j) into primary space and x_(j,s)=[x_(n) _(p) ₊₁(j), x_(n) _(p)₊₂(j), . . . , x_(n) _(p) _(+n) _(S) (j)] is the projection of x(j) intosecondary space.

The Mahalanobis distances in primary and secondary space are computedaccording to Equations 127 and 128 as follows:d _(j,p) ²(x(j))=(x _(j,p) −v _(j,p))·C _(j,p) ⁻¹·(x _(j,p) −v_(j,p))^(T)  (127)d _(j,s) ²(x(j))=(x _(j,s) −v _(j,s))·F _(j,s) ⁻¹·(x _(j,s) −v_(j,s))  (128)where

$F_{j,s} = {\frac{{tr}\left( C_{j,s} \right)}{n_{s}} \cdot {I_{n_{s} \times n_{s}}.}}$Then, the total distance is computed according to Equation 129 asfollows:d(x(j))=√{square root over (d _(j,p) ²(x(j))+d _(j,s) ²(x(j)))}{squareroot over (d _(j,p) ²(x(j))+d _(j,s) ²(x(j)))}  (129)

The DASCO probability of class assignment to class j is obtained bycomputing the Bayes' score according to Equations 130 and 131 asfollows:br(x(j))=d ²(x(j))−2 ln(r _(j))+ln(|det(C _(j,p))|)+n _(s)·ln(|det(Fr_(j,s))|)  (130)

$\begin{matrix}{{{Prob}\left( {{x(j)} \in \;{{Class}\mspace{14mu} j}} \right)} = \frac{\exp\left( {{- \frac{1}{2}}{{br}_{j}\left( {x(j)} \right)}} \right)}{\sum\limits_{k = 1}^{g}{\exp\left( {{- \frac{1}{2}}{{br}_{k}\left( {x(k)} \right)}} \right)}}} & (131)\end{matrix}$Equation 131 is evaluated for all classesj=1, 2, . . . g. DASCOclassification probabilities are computed thusly for each of theinterrogation points having a test reflectance spectrum, S(λ), that isnot eliminated in the Necrosis masking step (1424) or the hard“indeterminate” masking step (1426) in the embodiment shown in FIG. 74.

Probabilities determined using the DAFE classifier in step 1482 of FIG.74 and probabilities determined using the DASCO classifier in step 1484are combined and normalized in step 1486 to obtain for eachinterrogation point a set of statistical probabilities that the pointbelongs, respectively, to one of a number oftissue-class/state-of-health categories. In one embodiment, there arefive classes, as described above, including the following: (1) Normalsquamous (N_(s)) (2) CIN 1 (C₁), (3) CIN 2/3 (C₂₃), (4) Metaplasia (M),and (5) Columnar (C_(ol)) tissue.

The probability matrices P_(DAFE) and P_(DASCO) contain probabilityvectors corresponding to the interrogation points in the scan patternand are expressed as shown in Equations 132 and 133 as follows:

$\begin{matrix}{P_{DAFE} = \begin{bmatrix}{p_{{DAFE},1}(1)} & {p_{{DAFE},2}(1)} & \cdots & {p_{{DAFE},g}(1)} \\{p_{{DAFE},1}(2)} & {p_{{DAFE},2}(2)} & \cdots & {p_{{DAFE},g}(2)} \\\vdots & \vdots & \cdots & \vdots \\{p_{{DAFE},1}({nip})} & {p_{{DAFE},2}({nip})} & \cdots & {p_{{DAFE},g}({nip})}\end{bmatrix}} & (132) \\{P_{DASCO} = \begin{bmatrix}{p_{{DASCO},1}(1)} & {p_{{DASCO},2}(1)} & \cdots & {p_{{DASCO},g}(1)} \\{p_{{DASCO},1}(2)} & {p_{{DASCO},2}(2)} & \cdots & {p_{{DASCO},g}(2)} \\\vdots & \vdots & \cdots & \vdots \\{p_{{DASCO},1}({nip})} & {p_{{DASCO},2}({nip})} & \cdots & {p_{{DASCO},g}({nip})}\end{bmatrix}} & (133)\end{matrix}$where g is the total number of classes (for example, g=5); nip is thetotal number of interrogation points for which DAFE and DASCOprobabilities are calculated (for example, nip=up to 499); p_(DAFE,i)(j)represents the DAFE probability that the interrogation point j belongsto class i; and p_(DASCO,i)(j) represents the DASCO probability that theinterrogation point j belongs to class i.

Step 1486 of FIG. 74 represents the combination and normalization ofclassification probabilities determined by the DAFE and DASCOclassifiers in steps 1482 and 1484, respectively. Thecombined/normalized probability matrix, P_(COMB), is obtained bymultiplying the probability matrices P_(DAFE) and P_(DASCO) (Equations134 and 135) element-wise and dividing the row-wise product by the sumof each row's elements.

Combining Spectral and Image Data

The block diagram of FIG. 74 includes steps representing the combinationof spectral masks and image masks (1468, 1470, 1472, 1474), as well asthe application of the combined masks (1466, 1476, 1424, 1478, 1480,1424, 1426, 1428, 1430) in a tissue characterization system, accordingto one embodiment. These steps are discussed in more detail below.

As discussed above, the Necrosis_(spec) mask identifies interrogationpoints whose spectral data are indicative of necrotic tissue. Sincenecrosis is one of the categories in which interrogation points areclassified in step 1432 of FIG. 74, the Necrosis_(spec) mask is used notonly to eliminate interrogation points from further processing, but alsoto positively identify necrotic regions. Therefore, it is necessary tofilter out points affected by certain artifacts that may erroneouslycause a positive identification of necrosis.

Step 1466 of FIG. 74 indicates that two image masks are applied to thenecrosis spectral mask—the smoke tube mask, [ST]_(vid), 1450 and thespeculum mask, [SP]_(vid) 1452. Regions in which a speculum or smoketube has been identified cannot be positively identified as necrotic.Thus, interrogation points having any portion covered by pixelsindicated by the smoke tube mask, [ST]_(vid), 1450 and/or the speculummask, [SP]_(vid), 1452 are identified as “Indeterminate” and areeliminated from the necrosis mask.

Following this treatment, the necrosis mask is then applied in thebroadband reflectance spectra classification sequence in step 1424 ofFIG. 74. Each interrogation point at which the necrosis mask applies isclassified as “Necrotic”. The broadband spectral data at theseinterrogation points are then eliminated from further processing, or,alternately, the results of the statistical classifiers at these pointsare ignored in favor of classification of the points as “Necrotic”.Similarly, the necrosis mask is applied in the NED (no evidence ofdisease) spectral classification sequence in step 1476 of FIG. 74. Eachinterrogation point at which the necrosis mask applies is classified as“Necrotic”. The NED_(spec) mask need not be computed for theseinterrogation points, or, alternately, the results of the NED_(spec)mask at these points may be ignored in favor of classification of thepoints as “Necrotic”.

Three image masks are combined to form a fluorescence hard mask, “FHard,” which is applied in the NED (no evidence of disease) spectralclassification sequence in step 1478 of FIG. 74. As discussedhereinabove, hard masking results in a characterization of“Indeterminate” at affected interrogation points, and no furtherclassification computations are necessary for such points. The combinedfluorescence hard mask, “F Hard,” 1468 is a combination of the threeimage masks shown in FIG. 74 (1448, 1450, 1452), according to Equation134 as follows:F Hard=[ROI]_(vid) OR [ST]_(vid) OR [SP]_(vid)  (134)The combined “F Hard” mask is applied in the NED spectral classificationsequence in step 1478 of FIG. 74. Each interrogation point at which the“F Hard” mask applies is classified as “Indeterminate”. The NED_(spec)mask is not computed for these interrogation points. The “F Hard” maskapplies for each interrogation point having any portion covered bypixels indicated by the “F Hard” combined image mask.

Two spectral masks and five image masks are combined to form a broadbandreflectance “hard” mask, which is applied in the broadband reflectancestatistical classification sequence in step 1426 of FIG. 74. Thecombined hard mask, “BB Hard”, 1474 uses the image masks [ST]_(vid),[SP]_(vid), [ROI]_(vid), and [VW]_(vid) (1450, 1452, 1448, 1454) as hardmasks, and also treats them as “anchors” to qualify the sections of thetwo spectral masks—[CE]_(spec) and [MU]_(spec) (1444, 1446)—that areused as hard masks. The outer rim of interrogation points in thespectral pattern is also used as an anchor to the spectral masks.Finally, the intersection of the fluid-and-foam image mask [FL]_(vid)(1456) and the mucus spectral mask [MU]_(spec) (1446) is determined andused as a hard mask in “BB Hard” (1474). Each interrogation point atwhich the “BB Hard” mask applies is classified as “Indeterminate”. Thebroadband spectral data at these interrogation points are theneliminated from further processing, or, alternately, the results of thestatistical classifiers at these points are ignored in favor ofclassification of the points as “Indeterminate”.

In one embodiment, the combined hard mask, “BB Hard,” 1474 of FIG. 74 isdetermined according to the following steps.

First, form a combined image processing hard mask IPHardIPMsk using allthe interrogation points (IP's) that have any portion covered by one ormore of the followng image masks: [ST]_(vid), [SP]_(vid), [VW]_(vid) and[ROI]_(vid). The combined mask is expressed as: IPHardIPMsk=[ST]_(vid)OR [SP]_(vid) OR [VW]_(vid) OR [ROI]_(vid). Extend IPHardIPMsk toinclude the level one and level two neighbors of the interrogationpoints indicated above. For example, each IP that is not on an edge has6 level one neighbors and 12 level two neighbors, as shown in the scanpattern 202 in FIG. 5. Let extIMHardIPMsk be the new mask. Add all outerrim interrogation points to extIMHardIPMsk to form anchorMsk. The rim isdefined by the following interrogation points for the 499-point scanpattern 202 shown in FIG. 5: 1–9, 17–20, 31–33, 47–48, 65–66, 84–85,104–105, 125–126, 147–148, 170, 193, 215–216, 239, 263, 286–287, 309,332, 354–355, 376–377, 397–398, 417–418, 436–437, 454–455, 469–471,482–485, 493–499. Form a combined spectral mask SpecIPMsk using all theinterrogation points that are marked as either [CE]_(spec) or[MU]_(spec) (or both). Intersect the image processing anchor mask andthe combined spectral mask to obtain SPHardMsk: SPHardMsk=anchorMsk ANDSpecIPMsk. Intersect the image processing mask, [FL]_(vid), and spectralmucus mask, [MU]_(spec), to obtain the fluid hard mask FluidHardIPMsk,FluidHardIPMsk=[FL]^(vid) AND ([MU]_(spec) OR [CE]_(spec)). Finally formthe final hard mask: BBHard=IPHardIPMsk OR SPHardMsk OR FluidHardIPMsk.

Two image masks—Blood_(vid) and Os_(vid) (1458, 1460)—are combined toform a fluorescence “soft” mask, “F soft,” 1470 which is applied in theNED spectral classification sequence in step 1480 of FIG. 74. Asdiscussed hereinabove, soft masking involves applying a weightingfunction to data from points identified by the mask in order to weightthe data according to the likelihood they are affected by an artifact.The mask “F soft” determines two weighting functions—pen_(blood)(IP) andpen_(os)(IP)—for interrogation points (IP's) that are at least partiallycovered by the image masks Blood_(vid) and Os_(vid) (1458, 1460). Asdiscussed hereinabove, a percentage coverage, a, is determined for eachinterrogation point according to the percentage of pixels correspondingto the interrogation point that coincide with the image mask. For theimage masks Blood_(vid) and Os_(vid), (1458, 1460), corresponding valuesα_(blood)(IP) and α_(os)(IP) are determined for each affectedinterrogation point, and Equations 135 and 136 are used to calculate thecorresponding weighting at these interrogation points:pen_(blood)(IP)=1−α_(blood)(IP)  (135)pen_(os)(IP)=1−α_(os)(IP)  (136)The application of pen_(blood)(IP) and pen_(os)(IP) in the NED spectralclassification sequence of step 1480 is discussed in more detail below.

Two image masks—Glare_(vid) and Mucus_(vid) (1462, 1464)—are combined toform a broadband reflectance “soft” mask, “BB soft”, 1472 which isapplied in the broadband reflectance statistical classification sequencein step 1428 of FIG. 74. As discussed hereinabove, soft masking involvesapplying a weighting function to data from points identified by the maskin order to weight the data according to the likelihood it is affectedby an artifact. The mask “BB soft” determines two weightingfunctions—pen_(glare)(IP) and pen_(mucus)(IP)—for interrogation points(IP's) that are at least partially covered by the image masksGlare_(vid) and Mucus_(vid) (1462, 1464). As discussed hereinabove, apercentage coverage, a, is determined for each interrogation pointaccording to the percentage of pixels corresponding to the interrogationpoint that coincide with the image mask. For the image masks Glare_(vid)and Mucus_(vid), (1462, 1464) corresponding values α_(glare)(IP) andα_(mucus)(IP) are determined for each affected interrogation point, andEquations 137 and 138 are used to calculate the corresponding penaltiesat these interrogation points:pen_(glare)(IP)=1−{α_(glare)(IP)}^(1/5)  (137)pen_(mucus)(IP)=1−α_(mucus)(IP)  (138)The application of pen_(glare)(IP) and pen_(mucus)(IP) in the broadbandreflectance statistical classification sequence at step 1428 isdiscussed in more detail below.

The tissue-class/state-of-health classification of interrogation pointsincludes the application of masks as determined above. These steps areshown in FIG. 74. The tissue-class/state-of-health classification methodincludes an NED (no evidence of disease) spectral classificationsequence, as well as a broadband reflectance statistical classificationsequence, that apply the combined hard masks and soft masks describedabove. As discussed hereinabove, the separate identification of necroticregions and NED regions based on at least partially heuristic techniquesallows for the development of a statistical classifier that concentrateson identifying tissue less conducive to heuristic classification, forexample, CIN 2/3 tissue. Furthermore, by eliminating data affected byartifacts, the statistical classifiers are further improved, leading toimproved sensitivity and specificity of the final classification of atissue sample.

The Necrosis mask (1424, 1476), “BB Hard” mask (1426), and “F Hard” mask(1478) are applied as shown in FIG. 74. Interrogation points coincidingwith these masks are identified as either “Necrotic” or “Indeterminate”,as discussed hereinabove. In one embodiment, these regions are removedfrom further consideration. The NED classification sequence then appliesthe “F Soft” mask in step 1480. This is performed as explained below.

The NED_(spec) mask identifies interrogation points that indicate normalsquamous tissue, which is class (1) of the five classes used by the DAFEand DASCO classifiers discussed previously. The NED_(spec) mask assignsat each indicated (masked) interrogation point a probability vectorp_(s)=[1, 0, . . . , 0], where the normal squamous classificationprobability, N_(s) (class 1), is set equal to 1 and all other classprobabilities are set equal to 0. The “F Soft” mask is applied in step1480 by multiplying the N_(s) probability of indicated (masked) NEDinterrogation points by the product of the blood and os weightingfunctions, pen_(blood)(IP)·pen_(os)(IP). Hence, the normal squamousclassification probability, Ns, at these points will be less than 1.0.If the product, pen_(blood)(IP)·pen_(os)(IP), is equal to 0, then theinterrogation point IP is classified as “Indeterminate”. The NED_(spec)mask probability vector p_(s)=0 for all other interrogation points. Itis noted that if an interrogation point is not identified by theNED_(spec) mask, its N_(s) probability calculated by the broadbandreflectance statistical classification sequence is unaffected. Theapplication of the overall NED_(spec) mask is explained below in thediscussion of step 1430 in FIG. 74.

The broadband reflectance statistical classification sequence appliesthe Necrosis mask (1424) and the “BB Hard” mask (1426) beforedetermining statistical classification probabilities in steps 1482,1484, and 1486. As discussed above, the output of the broadbandstatistical classification is the probability matrix, P_(COMB), made upof probability vectors for the interrogation points, each vectorindicating respective probabilities that a given interrogation pointbelongs to one of the five tissue-class/state-of-health categories—(1)Normal squamous (N_(s)) (2) CIN 1 (C₁), (3) CIN 2/3 (C₂₃), (4)Metaplasia (M), and (5) Columnar (C_(ol)) tissue. The broadbandreflectance statistical classification sequence then applies the “BBSoft” mask in step 1428 by multiplying all five probabilities for eachaffected (masked) interrogation point by the quantitypen_(glare)(IP)·pen_(mucus)(IP).

Step 1432 of FIG. 74 classifies each interrogation point as Necrotic,CIN 2/3, NED, or Indeterminate. In one embodiment, the probabilities inP_(COMB) that correspond to CIN 2/3 classification, p_(COMB,C23)(IP)[class 3], are considered indicative of “CIN 2/3” classification in step1432, and all other classification categories in P_(COMB)—classes 1, 2,4, and 5 (N_(s), C₁, M, and C_(ol))—are considered indicative of “NED”tissue. In an alternative embodiment, further classificationdistinctions are made in step 1432.

In step 1430 of FIG. 74, the results of the NED_(spec) mask are appliedto the broadband reflectance-based classifications, P_(COMB). The“Necrotic” interrogation points and the hard-masked “Indeterminate”points have been identified and removed before step 1430. In step 1430,the remaining interrogation points are either classified as“Indeterminate” or are assigned a value of CIN 2/3 classificationprobability, p_(C23)(IP). Here, p_(C23)(IP) is the CIN 2/3classification probability for interrogation point IP that is set as aresult of step 1430. Interrogation points that are not identified by theNED_(spec) mask have been assigned NED_(spec) mask probability vectorp_(s)=0, and p_(C23)(IP)=p_(COMB,C23)(IP) for these points.Interrogation points that are identified by the NED mask have p_(s)=[1,0, . . . , 0], or p_(s)=[{pen_(blood)(IP)·pen_(os)(IP)}, 0, . . . , 0],(where p_(s,Ns)(IP)=1 or pen_(blood)(IP)·pen_(os)(IP)) depending onwhether the point has been penalized or not by the “F Soft” mask in step1480. The following describes how values of p_(C23)(IP) are determinedfor interrogation pionts that are identified by the NED_(spec) mask:

-   -   Due to spectral arbitration in step 128 of FIG. 74, the        broadband signal may have been suppressed for some interrogation        points, and only fluorescence spectra arc available. For these        interrogation points, the following rules are applied in step        1430 of FIG. 74:        -   1. IF p_(s,Ns)(IP)>0, THEN p_(C23)(IP)=0.        -   2. ELSE the interrogation point IP is classified as            “Indeterminate”.    -   For points having a valid arbitrated broadband signal and        fluorescence signal, the following fules are applied in step        1430 of FIG. 74:        -   1. IF p_(s,Ns)(IP)=1, THEN p_(C23)(IP)=0.        -   2. IF p_(s,Ns)(IP)=0, THEN p_(C23)(IP)=p_(COMB,C23)(IP).        -   3. IF p_(s,Ns)(IP)<1, THEN: IF p_(s,Ns)(IP)<p_(COMB,Ns)(IP),            THEN p_(C23)(IP)=p_(COMB,C23)(IP), ELSE, p_(C23)(IP)=0.

Step 1432 of FIG. 74 classifies each interrogation point as Necrotic,CIN 2/3, NED, or Indeterminate. Necrotic and hard-masked Indeterminateinterrogation points are identified prior to step 1430, as describedabove. In step 1430, the remaining interrogation points are eitherclassified as Indeterminate or are assigned a value of p_(C23)(IP). Forthese points, if p_(C23)(IP)=0, the point is classified as NED. Ifp_(C23)(IP)>0, the point is considered to have a non-zero probability ofhigh grade disease (CIN 2/3). In one embodiment, disease display (step138 of FIG. 74) uses these non-zero p_(C23)(IP) values to distinguishregions having low probability of CIN 2/3 and regions having highprobability of CIN 2/3.

Step 1434 of FIG. 74 represents post-classification processing. In oneembodiment, this includes a final clean-up step to remove isolated CIN2/3-classified interrogation points on the outer rim of the spectralscan pattern (for example, the outer rim consists of the numberedinterrogation points listed hereinabove. A CIN 2/3-classifiedinterrogation point is considered isolated if it has no direct, level-1neighbors that are classified as CIN 2/3. Such isolated points arere-classified as “Indeterminate” in step 1434 of FIG. 74.

Image Enhancement

The brightness of an acquired image of a tissue sample may change frompatient to patient due to obstructions, tissue type, and other factors.As a result, some images may be too dark for adequate visual assessment.Step 126 of the tissue characterization system 100 of FIG. 1 performs animage visual enhancement method to improve the image visual quality,using an image intensity transformation method. The improved image maythen be used, for example, in the disease display of step 138 of FIG. 1.

In one embodiment, the visual enhancement method of step 126 in FIG. 1involves analyzing the histogram of the luminance values of an inputimage, determining luminance statistics using only portions of the imagecorresponding to tissue, and performing a piecewise lineartransformation to produce a visually enhanced image. Step 126 involvesusing the image masks, as shown in step 108 of FIGS. 1 and 73 and asdescribed previously, in order to determine which portions of the imageare used to compute the image statistics. Step 126 includes performingbrightness and contrast enhancement, as well as applying image featureenhancement to improve local image features such as edges, borders, andtextures of different tissue types. Finally, a color balancingcorrection is applied to reduce the redness in certain images.

The visual enhancement method of step 126 includes determining whichportions of the input tissue image correspond to tissue in the region ofinterest, as opposed to artifacts such as glare, mucus, a speculum, theos, blood, smoke tube, and/or areas outside the region of interest. Onlythe regions corresponding to tissue of interest are used in determiningluminance statistics used in performing the visual enhancement. In oneembodiment, the image masks of FIGS. 73 and 74 are used to determine theportion of the image corresponding to tissue of interest. In oneembodiment, this image portion is [tROI]_(vid), a subset of the[ROI]_(vid) mask, computed in Equation 139 as follows:[tROI] _(vid) =[ROI] _(vid)−{[Glare]_(vid) +[SP] _(vid) +[os]_(vid)+Blood_(vid)+Mucus_(vid)+[ST]_(vid)}  (139)where the image masks above are as shown in FIG. 74 and as describedabove.

FIGS. 113A–C show graphs representing a step in a method of image visualenhancement in which a piecewise linear transformation of an input imageproduces an output image with enhanced image brightness and contrast. Ahistogram 2328 is computed for the luminance values μ (2326) of pixelswithin [tROI]_(vid) of an input image, and the histogram is used todetermine parameters of a piecewise linear transformation shown in theplot 2324 of FIG. 113B. The transformation produces luminance values v(2330) of a corresponding brightness- and contrast-enhanced outputimage. The transformed image generally has a wider range of luminancevalues, stretching from the minimum intensity (0) to the maximumintensity (255), than the input image. The luminance values from theinput image are transformed so that input luminance values within agiven range of the mean luminance are stretched over a wider range ofthe luminance spectrum than input luminance at the extremes. In oneembodiment, the piecewise linear transformation is as shown in Equation140:

$\begin{matrix}{v = \left\{ \begin{matrix}{{\alpha\;\mu},} & {L_{\min} \leq \mu < \mu_{a}} \\{{{\beta\left( {\mu - \mu_{a}} \right)} + v_{a}},} & {\mu_{a} \leq \mu < \mu_{b}} \\{{{\gamma\left( {\mu - \mu_{b}} \right)} + v_{b}},} & {\mu_{b} \leq \mu < L_{\max}}\end{matrix} \right.} & (140)\end{matrix}$where L_(max) is the maximum luminance value of a pixel within[tROI]_(vid) of the input image; the parameters μ_(a), μ_(b), v_(a), andv_(b) are piecewise linear breakpoints; and α,β, and γ are slopes of thetransformation.

In one embodiment, the image brightness and contrast enhancement isperformed according to the following steps. First, calculate theluminance L from the RGB signal of the input image using the formula:L=0.299*R+0.587*G+0.114*B. Extract the luminance image LROI within tROI([tROI]_(vid)): LROI=L AND tROI. Compute LROI mean, IMean. Compute thepiecewise linear breakpoints ma, mb, na, nb (μ_(a), μ_(b), v_(a), andv_(b)) from the LROI histogram, nHist[ ], as follows:

-   -   1. If ((IMean>38) AND (IMean<132)):        -   a. Compute and normalize nHist[ ] to the range [0, 1].        -   b. Compute ma and mb, the 5% and 98% histogram tails:            -   ma=i, if sum(nHist [i])>0.05, i=0 to 255.            -   mb=i, if sum(nHist [i])>0.98, i=0 to 255.        -   c. Define the expected low and high intensity parameter na            and nb:        -   d. na=Oandnb=180.    -   2. If (IMean>38 AND (IMean<132) AND ((ma≧na AND ma<100 AND        nb>20)), compute the slope or the degree of enhancement, bcDOE:        -   bcDOE=(nb−na)/(mb−ma).    -   3. If ((IMean>38) AND (IMean<132)), apply brightness and        contrast enhancement transformation to input color image in RGB        to obtain bcRGB (brightness and contrast enhanced color image).

In addition to producing an output image with enhanced image brightnessand contrast, the visual enhancement method of step 126 (FIG. 1) alsoincludes performing an image feature (local contrast) enhancement of theoutput image to emphasize high frequency components such as edges andfine features for the purposes of visual inspection. In one embodiment,image feature enhancement is performed using a spatial filteringtechnique according to Equations 141 and 142 as follows:I _(out)(m,n)=I _(in)(m,n)+ρG(m,n)  (141)G(m,n)=I _(in)(m,n)−S(m,n)  (142)where G(m, n) is the gradient image; p is the degree of the enhancement;I_(in)(m, n) and I_(out)(m, n) are the original and the resultant imageof the feature enhancement operation; and S(m, n) is the smoothed(lowpass filtered) version of I_(in)(m, n).

In one embodiment, the image feature enhancement operation of the visualenhancement method of step 126 is performed according to the followingsteps:

If IMean>38:

-   -   1. Smooth bcRGB (brightness and contrast enhanced color image)        with a 7×7 boxcar filter to obtain smRGB.    -   2. Subtract smRGB from bcRGB to obtain the gradient image,        grRGB.    -   3. Dilate glareMsk twice to obtain dGlareMsk=dil(glareMsk, 2).    -   4. Remove dilated glare regions form gradient image to avoid        emphasizing glare regions:        -   a. Convert gray image dGlareMsk to RGB image, dGlareMskC.        -   b. Remove glare image from gradient image to obtain grRGBgl:            grRGBgl=grRGB−dGlareMskC.    -   5. Define the degree of feature enhancement, feDOE, from        experiments, feDOE=0.8.    -   6. Scale grRGBgl by feDOE to obtain feRGB.    -   7. Add feRGB to bcRGB to produce image feature enhanced image        fRGB.

In addition to producing an output image with enhanced image brightness,contrast, and image features, the visual enhancement method of step 126(FIG. 1) also includes performing color balancing to reduce redness incertain overly-red tissue images, based on a mean-red-to-mean-blueratio.

In one embodiment, the color balancing operation of the visualenhancement method of step 126 is performed according to the followingsteps:

If IMean>38:

-   -   1. Split RGB (i.e. of the image feature enhanced image fRGB)        into R, G, B.    -   2. Extract the R image (within the tROIMsk) and compute mean        tissue redness, tRed.    -   3. Extract the B image (within the tROIMsk) and compute mean        tissue blueness tBlue.    -   4. Compute the red-blue ratio as RBRat=tRed/tBlue.    -   5. Perform color balancing:        -   If RBRat<1.20, no red redection.        -   Else if RBRat>=1.20 AND RBRat<1.32, R=0.95*R.        -   Else if RBRat>=1.32 AND RBRat<1.55, R=0.90*R.        -   Else if RBRat>=1.55, R=0.85*v.    -   6. Combine the R, G and B channels to form the final color image        for display.

Diagnostic Display

In one embodiment, the tissue characterization system 100 of FIG. 1comprises producing a disease probability display 138 for a reference(base) image of a test tissue sample using the interrogation pointclassifications in step 1432 of FIG. 74—Necrotic, CIN 2/3, NED, andIndeterminate. A method of disease probability display 138 includesproducing an output overlay image with annotations for indeterminateregions, necrotic regions, and/or regions of low-to-high probability ofhigh-grade disease, according to the classifications determined in step1432 of FIG. 74 for a given patient scan. The annotations are shown asan overlay on top of the reference tissue image to provideeasily-discernible tissue classification results, for example,indicating regions of concern for the purposes of biopsy, treatment,diagnosis, and/or further examination.

In one embodiment, indeterminate regions are indicated by a gray“see-through” crosshatch pattern that only partially obscures theunderlying reference image. Necrotic regions are indicated by a greentrellis pattern. Regions of tissue associated with high-grade disease(for example, CIN 2/3) are indicated by patches of contrasting colorwhich intensify according to the likelihood of high-grade disease.

In one embodiment, the disease probability display method 138 of FIG. 74as applied to a reference image of tissue from a patient scan includesthe following steps: determining a disease display layer from theclassification results of step 1432, overlaying the disease displaylayer on the reference image, determining an “indeterminate” mask fromthe classification results, overlaying the indeterminate mask on thedisease display image using a gray crosshatch pattern, determining a“necrosis” mask from the classification results, and overlaying thenecrosis mask on the disease display image using a green trellispattern. The result of the disease probability display method 138 ofFIG. 74 is a state-of-health “map” of the tissue sample, withannotations indicating indeterminate regions, necrotic regions, and/orregions of low-to-high probability of high-grade disease.

FIG. 114B represents an exemplary image of cervical tissue 2358 obtainedduring a patient examination and used as a reference (base) image inconstructing an output overlay image in the disease probability displaymethod 138 in FIG. 74. FIG. 114B shows the output overlay image 2360produced by the disease probability display method 138 in FIG. 74 thatcorresponds to the reference image 2358 in FIG. 114A. The output overlayimage 2360 in FIG. 114B contains annotations indicating indeterminateregions (2366), regions associated with a low probability of CIN 2/3(2362), and regions associated with a high probability of CIN 2/3(2364).

The disease probability display method 138 begins with the determinationof a disease display layer from the CIN 2/3 classification results ofstep 1432 in FIG. 74. In step 1432, values of p_(C23)(IP) are determinedfor interrogation points having a non-zero probability of high-gradedisease (here, CIN 2/3). An area of tissue indicative of high-gradedisease is represented on the disease display layer as an area whosecolor varies from yellow-to-blue, depending on values of p_(C23)(IP) atcorresponding interrogation points. The yellow color represents lowprobability of high-grade disease, and the blue color represents highprobability of high-grade disease. At the low end of the probabilityrange, the yellow color is blended into the reference image so thatthere is no sharp discontinuity between the high-grade disease regionand the image. In one embodiment, a minimum cut-off probability,p_(C23min)(IP), is set so that interrogation points with values ofp_(C23)(IP) lower than the minimum cut-off do not show on the diseasedisplay layer. In one embodiment, p_(C23min)(IP)=0.2.

FIGS. 115A and 115B represent two stages in the creation of a diseasedisplay layer, according to one embodiment. FIG. 115A shows the diseasedisplay layer 2368 wherein high-grade disease probabilities arerepresented by circles with intensities scaled by values of p_(C23)(IP)at corresponding interrogation points. In order to more realisticallyrepresent regions of high-grade disease on the tissue sample, thecircles in FIG. 115A are replaced with cones, then filtered to producethe disease display layer 2372 shown in FIG. 115B.

Finally, the grayscale intensity values are converted to a color scaleso that regions of high-grade disease appear on the overlay image aspatches of contrasting color that intensify according to the likelihoodof disease.

In one embodiment, the disease probability display method 138 of FIG. 1includes creating a disease display layer according to the followingsteps:

-   -   1. Retrieve the reference image (base image).    -   2. If all IPs are indeterminate, skip to creating the        Indeterminate Mask.    -   3. Generate CIN 2/3 probability image, I_(p), of base image        size, for all non-indeterminate IPs:        -   a. Generate a regular truncated cone centered at (15,15) on            a square matrix of size 29-by-29, set to 0:            -   i. The two truncating circles are centered around                (15,15) and have a radius R₀=14 and R_(i)=6.            -   ii. For each cone point, cone(i, j), let R be the                distance from the geometric center (15,15).                -   1. If R>R₀, cone(i,j)=0.                -   2. If R<R_(i), cone(i,j)=1.                -   3. If R_(i)<=R<=R₀, cone(i,j)=(R₀−R/(R₀−R_(i)).        -   b. Initialize I_(p) to 0.        -   c. For each IP with probability p_(C23)(IP)≧0.2:            -   i. make a copy of the cone;            -   ii. scale it by p;            -   iii. add it to I_(p) with the cone's center aligned with                the IP location.        -   d. Smooth I_(p) using a 33 by 33 separable symmetric Hamming            window filter specified by:            -   i. the following coefficients (since the filter is                symmetric around the origin, only 0.17 coefficients are                specified below; the others are the mirror image around                1.0):                -   (0.0800, 0.0888, 0.1150, 0.1575, 0.2147, 0.2844,                    0.3640, 0.4503                -   0.5400, 0.6297, 0.7160, 0.7956, 0.8653, 0.9225,                    0.965, 0.9912, 1.0);            -   ii. a gain of (0.85/301.37)^(1/2) for the 33 point ID                filter.        -   e. Linearly rescale I_(p) from the [0.2 1] range to the [0            1] range.        -   f. Clip rescaled I_(p) to range [0 1].    -   4. Compute an RGB colormap image and an alpha blending channel        from the probability image I_(p). The colormap defines a        transformation from integer intensity values in the range        [0,255] to an RGBα image.        -   a. The R colormap is a piecewise linear map specified by the            following breakpoints [0,255], [97,220], [179,138] and            [255,0].        -   b. The G colormap is a piecewise linear map specified by the            following breakpoints [0,0], [81,50], [210,162] and            [255,92].        -   c. The B colormap is a piecewise linear map specified by the            following breakpoints [0,255], [120,225], [178,251] and            [255,255].        -   d. The α colormap is a piecewise linear map specified by the            following breakpoints [0,255], [120,225], [178,251] and            [255,255].        -   e. Convert the floating point I_(p) image to an 8-bit image,            in the range [0,255] by rounding the product of each I_(p)            image pixel by 255.        -   f. Use the tissue colormap to get RGBα pixel values for the            disease display layer.

FIG. 116 shows the color transformation used in overlaying the diseasedisplay layer onto the reference image, as in the overlay image 2360 ofFIG. 114B. The first colorbar 2374 in FIG. 116 shows the blended colorsfrom yellow to blue that correspond to values of disease probabilityp_(C23)(IP), depicted on the x-axis 2375. A color corresponding to theaverage tissue color is determined, as shown in colorbar 2378. Theaverage tissue color is blended into the probability-correlatedyellow-to-blue colorbar 2374 so that the yellow color is blended intothe reference image where the disease probability, as indicated by thefiltered disease display layer, is low. This avoids a sharpdiscontinuity between the disease map and the tissue. In one embodiment,the disease display layer and the base (reference) image are combined byusing alpha-channel blending, where the alpha channel is as shown instep #4 of the above method to create a disease display layer. Thedisease display layer is overlaid upon the base image with blendingcontrolled by the computed alpha channel values according to Equation143 as follows:(Overlay Image Pixel)=α·(Disease Display Layer Pixel)+(1−α)·(Base ImagePixel)  (143)

Next, the disease probability display method 138 of FIG. 1 includesdetermining an “indeterminate” mask from the classification results instep 1432 of FIG. 74, where indeterminate regions are indicated by agray “see-through” crosshatch pattern. For an exemplary reference image,interrogation points classified as “Indeterminate” in step 1432 of FIG.74 indicate where the indeterminate mask is activated. The indeterminatecrosshatch mask is then combined with the output overlay image, as isshown in the overlay image 2360 of FIG. 114B. Here, indeterminateregions 2366 are indicated in shadowed regions around the edge of thetissue sample.

In one embodiment, the disease probability display method 138 of FIG. 1includes creating an indeterminate crosshatch mask according to thefollowing steps:

-   -   1. Create image, msk, of base image size and set to 0.    -   2. Draw disks of radius 0.75 mm centered at the coordinate of        each indeterminate interrogation point.    -   3. Erode mask image 3 times to obtain erodMsk=erod(msk, 3).    -   4. Compute image binary perimeter, perMsk, of erodMsk:        perMsk=not (erod(erodMsk, 1)) AND (erodMsk)), 1).    -   5. Compute indeterminate crosshatch mask:        -   a. Retrieve crosshatch image, xhatch, defined by a            horizontal pitch of 10 pixels, a vertical pitch of 20            pixels, a crosshatch slope of 2 and a grey value of            (166,166,166).        -   b. Perform logical OR of erodMsk and xhatch to obtain            xhatchMsk.        -   c. Perform logical OR of xhatchMsk with perMsk.

Next, the disease probability display method 138 of FIG. 1 includesdetermining a “necrosis” mask from the classification results in step1432 of FIG. 74, where necrotic regions are indicated by a green“see-through” trellis pattern. FIG. 117A depicts an exemplary referenceimage 2388 of cervical tissue having necrotic regions. For an exemplaryreference image, interrogation points classified as “Necrotic” in step1432 of FIG. 74 indicate where the “necrosis” mask is activated. Anecrosis trellis mask is included in the overlay image, as is shown inthe overlay image 2396 of FIG. 117B.

In one embodiment, the disease probability display method 138 of FIG. 1includes creating a necrosis trellis mask according to the followingsteps:

-   -   1. Create image, msk, of base image size, and set it to 0.    -   2. Draw disks of radius 0.75 mm centered at the coordinate of        each necrotic tissue interrogation point.    -   3. Erode mask image 3 times to obtain erodMsk erod(msk, 3).    -   4. Compute image binary perimeter, perMsk, of erodMsk:        perMsk=not (erod(erodMsk, 1)) AND (erodMsk)), 1).    -   5. Compute necrotic tissue trellis mask:        -   a. Retrieve trellis image, trellis, defined by a horizontal            pitch of 8 pixels, a vertical pitch of 8 pixels, a line            thickness of 2 and a green value of (0,255,104).        -   b. Perform logical OR of erodMsk and xhatch to obtain            trellisMsk.        -   c. Perform logical OR of trellisMsk with perMsk.

The result of the disease probability display method 138 of FIG. 74 is astate-of-health “map” of a tissue sample, with annotations indicatingindeterminate regions, necrotic regions, and/or regions of low-to-highprobability of high-grade disease. The disease display overlay imagescontain indeterminate regions and regions of low-to-high probability ofCIN 2/3.

In one embodiment, the disease display overlay image is producedimmediately following a patient scan in which spectral and image dataare acquired and processed. This allows a physicial to provideon-the-spot diagnostic review immediately following the scan.

Equivalents

While the invention has been particularly shown and described withreference to specific preferred embodiments, it should be understood bythose skilled in the art that various changes in form and detail may bemade therein without departing from the spirit and scope of theinvention as defined by the appended claims.

1. A method of displaying diagnostic data, the method comprising thesteps of: (a) providing a reference image of a tissue sample, saidtissue sample comprising epithelial cells; (b) providing a tissue-classprobability corresponding to each member of a plurality of regions ofsaid tissue sample, wherein said tissue-class probability is aprobability that a region comprises tissue of a predetermined type,wherein said type is selected from the group consisting of CIN 1, CIN 2,CIN 3, GIN 2/3, metaplasia, NED, and cancer; (c) creating a visualindication of said tissue-class probabilities using color values thatcorrespond to said tissue-class probabilities, wherein said color valuesvary over a range according to said tissue-class probabilities; and (d)displaying said reference image with said visual indication.
 2. Themethod of claim 1, wherein said creating step comprises assigninggrayscale values as a proxy for said tissue-class probabilities.
 3. Themethod of claim 1, wherein said creating step comprises assigning RGBcolor values as a proxy for said tissue-class probabilities.
 4. Themethod of claim 1, wherein said creating step comprises assigninggrayscale luminance values to said tissue-class probabilities andconverting said luminance values to RGB color values.
 5. The method ofclaim 1, wherein said creating step comprises blending colors to providediagnostically relevant information.
 6. The method of claim 5, whereinat least one of said colors is yellow.
 7. The method of claim 5, whereinat least one of said colors is blue.
 8. The method of claim 5, whereinsaid colors comprise a continuum from yellow to blue.
 9. The method ofclaim 8, wherein said continuum varies from an average tissue color to afirst reference color.
 10. The method of claim 1, wherein said creatingstep comprises assigning grayscale luminance values to said tissue-classprobabilities, spatially filtering said grayscale luminance values, andconverting said filtered grayscale luminance values to RGB color values.11. The method of claim 1, wherein said creating step comprisesspatially filtering values of said tissue-class probabilities, assigninggrayscale luminance values to said filtered probability values, andconverting said grayscale luminance values to RGB color values.
 12. Themethod of claim 1, wherein said visual indication identifies at leastone indeterminate region of said tissue sample.
 13. The method of claim12, wherein said visual indication identifies an indeterminate regionwithout obscuring a corresponding portion of said reference image. 14.The method of claim 12, wherein said indeterminate region is identifiedusing a crosshatch pattern or a trellis pattern.
 15. The method of claim1, wherein said visual indication identifies at least one necroticregion of said tissue sample.
 16. The method of claim 15, wherein saidvisual indication identifies a necrotic region without obscuring acorresponding portion of said reference image.
 17. The method of claim15, wherein said necrotic region is identified using a crosshatchpattern or a trellis pattern.
 18. The method of claim 1, wherein saiddisplaying step is performed in real time during a patient examination.19. The method of claim 1, wherein said displaying step is performedwithin about an hour of a patient examination.
 20. A method ofdisplaying diagnostic data, the method comprising the steps of: (a)providing a reference image of a tissue sample, said tissue samplecomprising epithelial cells; (b) providing a tissue-class probabilitycorresponding to each member of a plurality of regions of said tissuesample, wherein said tissue-class probability is a probability that aregion comprises tissue of a predetermined type, wherein said type isselected from the group consisting of CIN 1, CIN 2, CIN 3, CR4 2/3,metaplasia, NED, and cancer; (c) creating an overlay comprising colorsas a proxy for said tissue-class probabilities, wherein said overlayrepresents a range of tissue-class probabilities as a spectral blend ofcolorsthat varies over a range; and (d) displaying said reference imagewith said overlay.
 21. The method of claim 20, wherein said creatingstep comprises assigning grayscale luminance values to said tissue-classprobabilities, spatially filtering said grayscale luminance values, andconverting said filtered grayscale luminance values to RGB color values.22. A method of creating an overlay for displaying diagnostic data, themethod comprising the steps of: (a) providing a tissue-class probabilitycorresponding to each member of a plurality of regions of a tissuesample, said tissue sample comprising epithelial cells, wherein saidtissue-class probability is a probability that a region comprises tissueof a predetermined type, and wherein said type is selected from thegroup consisting of CIN 1, CIN 2, CiN 3, CIN 2/3, metaplasia, NED, andcancer; and (b) creating an overlay comprising colors as a proxy forsaid tissue-class probabilities, wherein said overlay represents a rangeof tissue-class probabilities as a spectral blend of colors that variesover a range.
 23. An apparatus for displaying diagnostic data, theapparatus adapted to: (a) provide a tissue-class probabilitycorresponding to each member of a plurality of regions of a tissuesample, said tissue sample comprising epithelial cells, wherein saidtissue-class probability is a probability that a region comprises tissueof a predetermined type, and wherein said type is selected from thegroup consisting of C114 1, GiN 2, CiN 3, GIN 2/3, metaplasia, NED, andcancer; and (b) create an overlay comprising colors as a proxy for saidtissue-class probabilities, wherein said overlay represents a range oftissue-class probabilities as a spectral blend of colorsthat varies overa range.
 24. The apparatus of claim 23, further adapted to display areference image of said tissue sample with said overlay.